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Kashyap GR, Sridhara S, Manoj KN, Gopakkali P, Das B, Jha PK, Prasad PVV. Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:1179-1197. [PMID: 38676745 DOI: 10.1007/s00484-024-02661-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/29/2024]
Abstract
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.
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Affiliation(s)
- Girish R Kashyap
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Shankarappa Sridhara
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India.
| | - Konapura Nagaraja Manoj
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Pradeep Gopakkali
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Bappa Das
- ICAR-Central Coastal Agricultural Research Institute, Old Goa, Goa, 403402, India
| | - Prakash Kumar Jha
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi, MS, USA
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
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Ahmad A, Liew AXW, Venturini F, Kalogeras A, Candiani A, Di Benedetto G, Ajibola S, Cartujo P, Romero P, Lykoudi A, De Grandis MM, Xouris C, Lo Bianco R, Doddy I, Elegbede I, D'Urso Labate GF, García del Moral LF, Martos V. AI can empower agriculture for global food security: challenges and prospects in developing nations. Front Artif Intell 2024; 7:1328530. [PMID: 38726306 PMCID: PMC11081032 DOI: 10.3389/frai.2024.1328530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/11/2024] [Indexed: 05/12/2024] Open
Abstract
Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.
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Affiliation(s)
- Ali Ahmad
- Research Institute for Integrated Coastal Zone Management, Polytechnic University of Valencia, Grau de Gandia, Valencia, Spain
| | | | - Francesca Venturini
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Winterthur, Switzerland
- TOELT LLC, Dübendorf, Switzerland
| | | | | | | | - Segun Ajibola
- Afridat UG, Bonn, Germany
- NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal
| | - Pedro Cartujo
- Department of Electronic and Computer Technology, University of Granada, Granada, Spain
| | - Pablo Romero
- GRANIOT Satellite Technologies S.L, Granada, Spain
| | | | | | - Christos Xouris
- Gaia Robotics Idiotiki Kefalaiouxiki Etaireia, Patras, Greece
| | - Riccardo Lo Bianco
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Palermo, Italy
| | - Irawan Doddy
- Department of Mechanical Engineering, Universitas Muhammadiyah Pontianak – Universitas, Kalimantan Barat, Indonesia
| | | | | | - Luis F. García del Moral
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
| | - Vanessa Martos
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
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Opara IK, Opara UL, Okolie JA, Fawole OA. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:1200. [PMID: 38732414 PMCID: PMC11085577 DOI: 10.3390/plants13091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
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Affiliation(s)
- Ikechukwu Kingsley Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria
| | - Jude A. Okolie
- Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Olaniyi Amos Fawole
- Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa
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Bernal-Gallardo JJ, de Folter S. Plant genome information facilitates plant functional genomics. PLANTA 2024; 259:117. [PMID: 38592421 PMCID: PMC11004055 DOI: 10.1007/s00425-024-04397-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/20/2024] [Indexed: 04/10/2024]
Abstract
MAIN CONCLUSION In this review, we give an overview of plant sequencing efforts and how this impacts plant functional genomics research. Plant genome sequence information greatly facilitates the studies of plant biology, functional genomics, evolution of genomes and genes, domestication processes, phylogenetic relationships, among many others. More than two decades of sequencing efforts have boosted the number of available sequenced plant genomes. The first plant genome, of Arabidopsis, was published in the year 2000 and currently, 4604 plant genomes from 1482 plant species have been published. Various large sequence initiatives are running, which are planning to produce tens of thousands of sequenced plant genomes in the near future. In this review, we give an overview on the status of sequenced plant genomes and on the use of genome information in different research areas.
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Affiliation(s)
- Judith Jazmin Bernal-Gallardo
- Unidad de Genómica Avanzada (UGA-Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
| | - Stefan de Folter
- Unidad de Genómica Avanzada (UGA-Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico.
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Aasim M, Yıldırım B, Say A, Ali SA, Aytaç S, Nadeem MA. Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H 2O 2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). PLANT MOLECULAR BIOLOGY 2024; 114:33. [PMID: 38526768 DOI: 10.1007/s11103-024-01427-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/14/2024] [Indexed: 03/27/2024]
Abstract
Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.
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Affiliation(s)
- Muhammad Aasim
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey.
| | - Buşra Yıldırım
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
| | - Ahmet Say
- Department of Agricultural Biotechnology, Faculty of Agriculture, Erciyes University, Kayseri, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Selim Aytaç
- Institute of Hemp Researches, Ondokuz Mayis University, Samsun, Turkey
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
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Zhou W, Yan Z, Zhang L. A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction. Sci Rep 2024; 14:5905. [PMID: 38467662 PMCID: PMC10928191 DOI: 10.1038/s41598-024-55243-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.
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Affiliation(s)
- Wei Zhou
- Florida Agricultural and Mechanical University, Tallahassee, FL, 32307, USA.
| | - Zhengxiao Yan
- Florida State University, Tallahassee, FL, 32306, USA
| | - Liting Zhang
- Florida State University, Tallahassee, FL, 32306, USA
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Salse J, Barnard RL, Veneault-Fourrey C, Rouached H. Strategies for breeding crops for future environments. TRENDS IN PLANT SCIENCE 2024; 29:303-318. [PMID: 37833181 DOI: 10.1016/j.tplants.2023.08.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 06/27/2023] [Accepted: 08/08/2023] [Indexed: 10/15/2023]
Abstract
The green revolution successfully increased agricultural output in the early 1960s by relying primarily on three pillars: plant breeding, irrigation, and chemical fertilization. Today, the need to reduce the use of chemical fertilizers, water scarcity, and future environmental changes, together with a growing population, requires innovative strategies to adapt to a new context and prevent food shortages. Therefore, scientists from around the world are directing their efforts to breed crops for future environments to sustainably produce more nutritious food. Herein, we propose scientific avenues to be reinforced in selecting varieties, including crop wild relatives, either for monoculture or mixed cropping systems, taking advantage of plant-microbial interactions, while considering the diversity of organisms associated with crops and unlocking combinatorial nutritional stresses.
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Affiliation(s)
- Jérôme Salse
- UCA-INRAE UMR 1095 Genetics, Diversity, and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000 Clermont-Ferrand, France
| | - Romain L Barnard
- Agroécologie, INRAE, Institut Agro, Université de Bourgogne, Université de Bourgogne Franche-Comté, 21000 Dijon, France
| | - Claire Veneault-Fourrey
- Université de Lorraine, INRAE, Unité Mixte de Recherche Interactions Arbres-Microorganismes, F-54000 Nancy, France
| | - Hatem Rouached
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48823, USA; The Plant Resilience Institute, Michigan State University, East Lansing, MI 48823, USA.
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Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. PLANTS (BASEL, SWITZERLAND) 2023; 12:4142. [PMID: 38140469 PMCID: PMC10747955 DOI: 10.3390/plants12244142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
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Affiliation(s)
- Miguel Damásio
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Miguel Barbosa
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - João Deus
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Eduardo Fernandes
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - André Leitão
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Luís Albino
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Filipe Fonseca
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - José Silvestre
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
- GREEN-IT Bioresources4sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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Bharati R, Sen MK, Severová L, Svoboda R, Fernández-Cusimamani E. Polyploidization and genomic selection integration for grapevine breeding: a perspective. FRONTIERS IN PLANT SCIENCE 2023; 14:1248978. [PMID: 38034577 PMCID: PMC10684766 DOI: 10.3389/fpls.2023.1248978] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.
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Affiliation(s)
- Rohit Bharati
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Madhab Kumar Sen
- Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Suchdol, Czechia
| | - Lucie Severová
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Roman Svoboda
- Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czechia
| | - Eloy Fernández-Cusimamani
- Department of Crop Sciences and Agroforestry, The Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Czechia
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Sen S, Woodhouse MR, Portwood JL, Andorf CM. Maize Feature Store: A centralized resource to manage and analyze curated maize multi-omics features for machine learning applications. Database (Oxford) 2023; 2023:baad078. [PMID: 37935586 PMCID: PMC10634621 DOI: 10.1093/database/baad078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 11/09/2023]
Abstract
The big-data analysis of complex data associated with maize genomes accelerates genetic research and improves agronomic traits. As a result, efforts have increased to integrate diverse datasets and extract meaning from these measurements. Machine learning models are a powerful tool for gaining knowledge from large and complex datasets. However, these models must be trained on high-quality features to succeed. Currently, there are no solutions to host maize multi-omics datasets with end-to-end solutions for evaluating and linking features to target gene annotations. Our work presents the Maize Feature Store (MFS), a versatile application that combines features built on complex data to facilitate exploration, modeling and analysis. Feature stores allow researchers to rapidly deploy machine learning applications by managing and providing access to frequently used features. We populated the MFS for the maize reference genome with over 14 000 gene-based features based on published genomic, transcriptomic, epigenomic, variomic and proteomics datasets. Using the MFS, we created an accurate pan-genome classification model with an AUC-ROC score of 0.87. The MFS is publicly available through the maize genetics and genomics database. Database URL https://mfs.maizegdb.org/.
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Affiliation(s)
- Shatabdi Sen
- Department of Plant Pathology & Microbiology, Iowa State University, 1344 Advanced Teaching & Research Bldg, 2213 Pammel Dr, Ames, IA 50011, USA
| | - Margaret R Woodhouse
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, 819 Wallace Road, Ames, IA 50011, USA
| | - John L Portwood
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, 819 Wallace Road, Ames, IA 50011, USA
| | - Carson M Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, 819 Wallace Road, Ames, IA 50011, USA
- Department of Computer Science, Iowa State University, Atanasoff Hall, 2434 Osborn Dr, Ames, IA 50011, USA
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Sharma N, Raman H, Wheeler D, Kalenahalli Y, Sharma R. Data-driven approaches to improve water-use efficiency and drought resistance in crop plants. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 336:111852. [PMID: 37659733 DOI: 10.1016/j.plantsci.2023.111852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.
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Affiliation(s)
- Niharika Sharma
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.
| | - Harsh Raman
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - David Wheeler
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia
| | - Yogendra Kalenahalli
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana 502324, India
| | - Rita Sharma
- Department of Biological Sciences, BITS Pilani, Pilani Campus, Rajasthan 333031, India
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Salaić M, Novoselnik F, Žarko IP, Galić V. Nitrogen deficiency in maize: Annotated image classification dataset. Data Brief 2023; 50:109625. [PMID: 37823068 PMCID: PMC10562141 DOI: 10.1016/j.dib.2023.109625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/02/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Nitrogen (N) is one of the key inputs in maize production applied in the form of fertilizers. Nitrogen deficiency during the vegetation period leads to lower yields since N is utilized in proteins and enzymes that enable important biochemical processes such as photosynthesis. Nitrogen deficiency leads to specific symptoms that eventually become visible to the naked eye during vegetation. Our hypothesis was that N deficiency can be detected from maize RGB images in parametric process such as a deep neural network. The aim of the reported dataset is to optimize the usage of N in the farmer's fields and accordingly, reduce its environmental footprint. This dataset contains 1200 images of maize canopy from field trials, annotated by an expert from an agricultural institution. The field trials included three levels of N fertilization: N0 without N fertilization, N75 with 75 kg of added N fertilizer, and NFull with 136 kg of added N fertilizer. For each fertilizer level, 400 plots were created with 238 different maize genotypes, resulting in a total of 1200 plots. Images were taken with a tripod mounted DSLR camera, aperture priority set to f/8 and sensor sensitivity set to ISO400. Images were taken at a 45° angle to each plot. This dataset can be useful to both researchers, data scientists and agronomists, especially in the context of emerging technologies in precision agriculture, such as robotics, 5G networks and unmanned aerial vehicle (UAV). The dataset is one of the first publicly accessible datasets of maize canopy images under different N fertilization levels and represents a valuable public resource for development of machine learning models for in-season detection of N deficiency in maize.
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Affiliation(s)
| | | | - Ivana Podnar Žarko
- University of Zagreb2, Faculty of Electrical Engineering and Computing, HR10000 Zagreb, Croatia
| | - Vlatko Galić
- Agricultural Institute Osijek, HR31000 Osijek, Croatia
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Cui Z, Li K, Kang C, Wu Y, Li T, Li M. Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset. PLANTS (BASEL, SWITZERLAND) 2023; 12:3280. [PMID: 37765444 PMCID: PMC10534746 DOI: 10.3390/plants12183280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Efficient image recognition is important in crop and forest management. However, it faces many challenges, such as the large number of plant species and diseases, the variability of plant appearance, and the scarcity of labeled data for training. To address this issue, we modified a SOTA Cross-Domain Few-shot Learning (CDFSL) method based on prototypical networks and attention mechanisms. We employed attention mechanisms to perform feature extraction and prototype generation by focusing on the most relevant parts of the images, then used prototypical networks to learn the prototype of each category and classify new instances. Finally, we demonstrated the effectiveness of the modified CDFSL method on several plant and disease recognition datasets. The results showed that the modified pipeline was able to recognize several cross-domain datasets using generic representations, and achieved up to 96.95% and 94.07% classification accuracy on datasets with the same and different domains, respectively. In addition, we visualized the experimental results, demonstrating the model's stable transfer capability between datasets and the model's high visual correlation with plant and disease biological characteristics. Moreover, by extending the classes of different semantics within the training dataset, our model can be generalized to other domains, which implies broad applicability.
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Affiliation(s)
| | | | | | | | | | - Mingyang Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.C.); (K.L.); (C.K.); (Y.W.); (T.L.)
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Zhang Y, Zhang N, Chai X, Sun T. Machine learning for image-based multi-omics analysis of leaf veins. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4928-4941. [PMID: 37410807 DOI: 10.1093/jxb/erad251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.
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Affiliation(s)
- Yubin Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Ning Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Tan Sun
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
- Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
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Duc NT, Ramlal A, Rajendran A, Raju D, Lal SK, Kumar S, Sahoo RN, Chinnusamy V. Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1206357. [PMID: 37771485 PMCID: PMC10523016 DOI: 10.3389/fpls.2023.1206357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/26/2023] [Indexed: 09/30/2023]
Abstract
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.
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Affiliation(s)
- Nguyen Trung Duc
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Ayyagari Ramlal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- School of Biological Sciences, Universiti Sains Malaysia (USM), Georgetown, Penang, Malaysia
| | - Ambika Rajendran
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Dhandapani Raju
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - S. K. Lal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
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Gerullis M, Pieruschka R, Fahrner S, Hartl L, Schurr U, Heckelei T. From genes to policy: mission-oriented governance of plant-breeding research and technologies. FRONTIERS IN PLANT SCIENCE 2023; 14:1235175. [PMID: 37731976 PMCID: PMC10507248 DOI: 10.3389/fpls.2023.1235175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023]
Abstract
Mission-oriented governance of research focuses on inspirational, yet attainable goals and targets the sustainable development goals through innovation pathways. We disentangle its implications for plant breeding research and thus impacting the sustainability transformation of agricultural systems, as it requires improved crop varieties and management practices. Speedy success in plant breeding is vital to lower the use of chemical fertilizers and pesticides, increase crop resilience to climate stresses and reduce postharvest losses. A key question is how this success may come about? So far plant breeding research has ignored wider social systems feedbacks, but governance also failed to deliver a set of systemic breeding goals providing directionality and organization to research policy of the same. To address these challenges, we propose a heuristic illustrating the core elements needed for governing plant breeding research: Genetics, Environment, Management and Social system (GxExMxS) are the core elements for defining directions for future breeding. We illustrate this based on historic cases in context of current developments in plant phenotyping technologies and derive implications for governing research infrastructures and breeding programs. As part of mission-oriented governance we deem long-term investments into human resources and experimental set-ups for agricultural systems necessary to ensure a symbiotic relationship for private and public breeding actors and recommend fostering collaboration between social and natural sciences for working towards transdisciplinary collaboration.
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Affiliation(s)
- Maria Gerullis
- Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, United States
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Roland Pieruschka
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Sven Fahrner
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Lorenz Hartl
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Ulrich Schurr
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Thomas Heckelei
- Institute for Food and Resource Economics, University of Bonn, Bonn, Germany
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18
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Simpson CJC, Singh P, Sogbohossou DEO, Eric Schranz M, Hibberd JM. A rapid method to quantify vein density in C 4 plants using starch staining. PLANT, CELL & ENVIRONMENT 2023; 46:2928-2938. [PMID: 37350263 PMCID: PMC10947256 DOI: 10.1111/pce.14656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023]
Abstract
C4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C4 plants compared with the ancestral C3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C4 species demonstrating natural variation for this trait.
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Affiliation(s)
| | - Pallavi Singh
- Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | | | - M. Eric Schranz
- Biosystematics GroupWageningen UniversityWageningenThe Netherlands
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Das P, Chandra T, Negi A, Jaiswal S, Iquebal MA, Rai A, Kumar D. A comprehensive review on genomic resources in medicinally and industrially important major spices for future breeding programs: Status, utility and challenges. Curr Res Food Sci 2023; 7:100579. [PMID: 37701635 PMCID: PMC10494321 DOI: 10.1016/j.crfs.2023.100579] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 08/26/2023] [Indexed: 09/14/2023] Open
Abstract
In the global market, spices possess a high-value but low-volume commodities of commerce. The food industry depends largely on spices for taste, flavor, and therapeutic properties in replacement of cheap synthetic ones. The estimated growth rate for spices demand in the world is ∼3.19%. Since spices grow in limited geographical regions, India is one of the leading producer of spices, contributing 25-30 percent of total world trade. Hitherto, there has been no comprehensive review of the genomic resources of industrially important major medicinal spices to overcome major impediments in varietal improvement and management. This review focuses on currently available genomic resources of 24 commercially significant spices, namely, Ajwain, Allspice, Asafoetida, Black pepper, Cardamom large, Cardamom small, Celery, Chillies, Cinnamon, Clove, Coriander, Cumin, Curry leaf, Dill seed, Fennel, Fenugreek, Garlic, Ginger, Mint, Nutmeg, Saffron, Tamarind, Turmeric and Vanilla. The advent of low-cost sequencing machines has contributed immensely to the voluminous data generation of these spices, cracking the complex genomic architecture, marker discovery, and understanding comparative and functional genomics. This review of spice genomics resources concludes the perspective and way forward to provide footprints by uncovering genome assemblies, sequencing and re-sequencing projects, transcriptome-based studies, non-coding RNA-mediated regulation, organelles-based resources, developed molecular markers, web resources, databases and AI-directed resources in candidate spices for enhanced breeding potential in them. Further, their integration with molecular breeding could be of immense use in formulating a strategy to protect and expand the production of the spices due to increased global demand.
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Affiliation(s)
- Parinita Das
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Tilak Chandra
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Ankita Negi
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dinesh Kumar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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Rodrigues V, Deusdado S. Meta-learning approach for bacteria classification and identification of informative genes of the Bacillus megaterium: tomato roots tissue interaction. 3 Biotech 2023; 13:271. [PMID: 37449251 PMCID: PMC10335980 DOI: 10.1007/s13205-023-03690-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/26/2023] [Indexed: 07/18/2023] Open
Abstract
Plant growth-promoting rhizobacteria (PGPRs) are bacteria that colonize the plant roots. These beneficial bacteria have an influence on plant development through multiple mechanisms, such as nutrient availability, alleviating biotic and abiotic stress, and secrete phytohormones. Therefore, their inoculation constitutes a powerful tool towards sustainable agriculture and crop production. To understand plant-PGPRs interaction we present the classification of PGPR using machine learning and meta-learning classifiers namely Support Vector Machine (SVM), Kernel Logistic Regression (KLR), meta-SVM and meta-KLR to predict the presence of Bacillus megaterium inoculated in tomato root tissues using publicly available transcriptomic data. The original dataset presents 36 significantly differentially expressed genes. As the meta-KLR achieved near-optimal performance considering all the relevant metrics, this meta learner was afterwards used to identify the informative genes (IGs). The outcomes showed 157 IGs, being present all significantly differentially expressed genes previously identified. Among the IGs, 113 were identified as tomato genes, 5 as Bacillus subtilis proteins, 1 as Escherichia coli protein and 6 were unidentified. Then, a functional enrichment analysis of the tomato IGs showed 175 biological processes, 22 molecular functions and 20 KEGG pathways involved in B. megaterium-tomato interaction. Furthermore, the biological networks study of their Arabidopsis thaliana orthologous genes identified the co-expression, predicted interaction, shared protein domains and co-localization networks. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03690-0.
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Affiliation(s)
| | - Sérgio Deusdado
- Instituto Politécnico de Bragança, CIMO—Centro de Investigação de Montanha, 5301-855 Bragança, Portugal
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Ruperao P, Rangan P, Shah T, Thakur V, Kalia S, Mayes S, Rathore A. The Progression in Developing Genomic Resources for Crop Improvement. Life (Basel) 2023; 13:1668. [PMID: 37629524 PMCID: PMC10455509 DOI: 10.3390/life13081668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques.
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Affiliation(s)
- Pradeep Ruperao
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Parimalan Rangan
- ICAR-National Bureau of Plant Genetic Resources, PUSA Campus, New Delhi 110012, India;
| | - Trushar Shah
- International Institute of Tropical Agriculture (IITA), Nairobi 30709-00100, Kenya;
| | - Vivek Thakur
- Department of Systems & Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India;
| | - Sanjay Kalia
- Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi 110003, India;
| | - Sean Mayes
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
| | - Abhishek Rathore
- Excellence in Breeding, International Maize and Wheat Improvement Center (CIMMYT), Hyderabad 502324, India
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Saqib MA, Aqib M, Tahir MN, Hafeez Y. Towards deep learning based smart farming for intelligent weeds management in crops. FRONTIERS IN PLANT SCIENCE 2023; 14:1211235. [PMID: 37575940 PMCID: PMC10416644 DOI: 10.3389/fpls.2023.1211235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/29/2023] [Indexed: 08/15/2023]
Abstract
Introduction Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops. Methods Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny). Results and discussion The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value. Future work In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.
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Affiliation(s)
- Muhammad Ali Saqib
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Muhammad Aqib
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
- National Center of Industrial Biotechnology, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Muhammad Naveed Tahir
- Department of Agronomy, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
- Pilot Project for Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
| | - Yaser Hafeez
- University Institute of Information Technology (UIIT), Pir Mehr Ali Shah (PMAS)-Arid Agriculture University Rawalpindi, Rawalpindi, Punjab, Pakistan
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Hao X, Cao Y, Zhang Z, Tomasetto F, Yan W, Xu C, Luan Q, Li Y. CountShoots: Automatic Detection and Counting of Slash Pine New Shoots Using UAV Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0065. [PMID: 38235123 PMCID: PMC10794053 DOI: 10.34133/plantphenomics.0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/12/2023] [Indexed: 01/19/2024]
Abstract
The density of new shoots on pine trees is an important indicator of their growth and photosynthetic capacity. However, traditional methods to monitor new shoot density rely on manual and destructive measurements, which are labor-intensive and have led to fewer studies on new shoot density. Therefore, in this study, we present user-friendly software called CountShoots, which extracts new shoot density in an easy and convenient way using unmanned aerial vehicles based on the YOLOX and Slash Pine Shoot Counting Network (SPSC-net) models. This software mainly consists of 2 steps. Firstly, we deployed a modified YOLOX model to identify the tree species and location from complex RGB background images, which yielded a high recognition accuracy of 99.15% and 95.47%. These results showed that our model produced higher detection accuracy compared to YOLOv5, Efficientnet, and Faster-RCNN models. Secondly, we constructed an SPSC-net. This methodology is based on the CCTrans network, which outperformed DM-Count, CSR-net, and MCNN models, with the lowest mean squared error and mean absolute error results among other models (i.e., 2.18 and 1.47, respectively). To our best knowledge, our work is the first research contribution to identify tree crowns and count new shoots automatically in slash pine. Our research outcome provides a highly efficient and rapid user-interactive pine tree new shoot detection and counting system for tree breeding and genetic use purposes.
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Affiliation(s)
- Xia Hao
- College of Information Science and Engineering, Shandong Agricultural University, No. 61, Daizong Road, Taian 271018, Shandong Province, China
| | - Yue Cao
- College of Information Science and Engineering, Shandong Agricultural University, No. 61, Daizong Road, Taian 271018, Shandong Province, China
| | - Zhaoxu Zhang
- College of Information Science and Engineering, Shandong Agricultural University, No. 61, Daizong Road, Taian 271018, Shandong Province, China
| | | | - Weiqi Yan
- Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand
| | - Cong Xu
- School of Forestry, University of Canterbury, Private Bag 4800, 8041 Christchurch, New Zealand
| | - Qifu Luan
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China
| | - Yanjie Li
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China
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24
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Paulus S, Leiding B. Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks? PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0070. [PMID: 37434757 PMCID: PMC10332799 DOI: 10.34133/plantphenomics.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/25/2023] [Indexed: 07/13/2023]
Affiliation(s)
- Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany
| | - Benjamin Leiding
- Institute for Software and Systems Engineering, TU Clausthal, Wallstr. 6, 38640 Goslar, Germany
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25
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Bhat JA, Feng X, Mir ZA, Raina A, Siddique KHM. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding. PHYSIOLOGIA PLANTARUM 2023; 175:e13969. [PMID: 37401892 DOI: 10.1111/ppl.13969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/11/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.
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Affiliation(s)
| | - Xianzhong Feng
- Zhejiang Lab, Hangzhou, China
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zahoor A Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Aamir Raina
- Department of Botany, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, Western Australia, Australia
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26
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Harandi N, Vandenberghe B, Vankerschaver J, Depuydt S, Van Messem A. How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques. PLANT METHODS 2023; 19:60. [PMID: 37353846 DOI: 10.1186/s13007-023-01031-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.
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Affiliation(s)
- Negin Harandi
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | | | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | - Stephen Depuydt
- Erasmus Applied University of Sciences and Arts, Campus Kaai, Nijverheidskaai 170, Anderlecht, Belgium
| | - Arnout Van Messem
- Department of Mathematics, Université de Liège, Allée de la Découverte 12, Liège, Belgium.
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27
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Huang Y. Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation. PLANTS (BASEL, SWITZERLAND) 2023; 12:2309. [PMID: 37375934 DOI: 10.3390/plants12122309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/04/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.
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Affiliation(s)
- Ying Huang
- Electronic Information School, Wuhan University, Wuhan 430072, China
- School of Automatic Control, Liuzhou Railway Vocational Technical College, Liuzhou 545616, China
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28
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Xin B, Sun J, Bartholomeus H, Kootstra G. 3D data-augmentation methods for semantic segmentation of tomato plant parts. FRONTIERS IN PLANT SCIENCE 2023; 14:1045545. [PMID: 37377799 PMCID: PMC10291624 DOI: 10.3389/fpls.2023.1045545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 04/28/2023] [Indexed: 06/29/2023]
Abstract
Introduction 3D semantic segmentation of plant point clouds is an important step towards automatic plant phenotyping and crop modeling. Since traditional hand-designed methods for point-cloud processing face challenges in generalisation, current methods are based on deep neural network that learn to perform the 3D segmentation based on training data. However, these methods require a large annotated training set to perform well. Especially for 3D semantic segmentation, the collection of training data is highly labour intensitive and time consuming. Data augmentation has been shown to improve training on small training sets. However, it is unclear which data-augmentation methods are effective for 3D plant-part segmentation. Methods In the proposed work, five novel data-augmentation methods (global cropping, brightness adjustment, leaf translation, leaf rotation, and leaf crossover) were proposed and compared to five existing methods (online down sampling, global jittering, global scaling, global rotation, and global translation). The methods were applied to PointNet++ for 3D semantic segmentation of the point clouds of three cultivars of tomato plants (Merlice, Brioso, and Gardener Delight). The point clouds were segmented into soil base, stick, stemwork, and other bio-structures. Results and disccusion Among the data augmentation methods being proposed in this paper, leaf crossover indicated the most promising result which outperformed the existing ones. Leaf rotation (around Z axis), leaf translation, and cropping also performed well on the 3D tomato plant point clouds, which outperformed most of the existing work apart from global jittering. The proposed 3D data augmentation approaches significantly improve the overfitting caused by the limited training data. The improved plant-part segmentation further enables a more accurate reconstruction of the plant architecture.
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Affiliation(s)
- Bolai Xin
- Department of Plant Science, Wageningen University and Research, Wageningen, Netherlands
| | - Ji Sun
- Department of Plant Science, Wageningen University and Research, Wageningen, Netherlands
| | - Harm Bartholomeus
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, Netherlands
| | - Gert Kootstra
- Department of Plant Science, Wageningen University and Research, Wageningen, Netherlands
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29
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Tang Z, You TT, Li YF, Tang ZX, Bao MQ, Dong G, Xu ZR, Wang P, Zhao FJ. Rapid identification of high and low cadmium (Cd) accumulating rice cultivars using machine learning models with molecular markers and soil Cd levels as input data. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 326:121501. [PMID: 36963454 DOI: 10.1016/j.envpol.2023.121501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/28/2023] [Accepted: 03/21/2023] [Indexed: 06/18/2023]
Abstract
Excessive accumulation of cadmium (Cd) in rice grains threatens food safety and human health. Growing low Cd accumulating rice cultivars is an effective approach to produce low-Cd rice. However, field screening of low-Cd rice cultivars is laborious, time-consuming, and subjected to the influence of environment × genotype interactions. In the present study, we investigated whether machine learning-based methods incorporating genotype and soil Cd concentration can identify high and low-Cd accumulating rice cultivars. One hundred and sixty-seven locally adapted high-yielding rice cultivars were grown in three fields with different soil Cd levels and genotyped using four molecular markers related to grain Cd accumulation. We identified sixteen cultivars as stable low-Cd accumulators with grain Cd concentrations below the 0.2 mg kg-1 food safety limit in all three paddy fields. In addition, we developed eight machine learning-based models to predict low- and high-Cd accumulating rice cultivars with genotypes and soil Cd levels as input data. The optimized model classifies low- or high-Cd cultivars (i.e., the grain Cd concentration below or above 0.2 mg kg-1) with an overall accuracy of 76%. These results indicate that machine learning-based classification models constructed with molecular markers and soil Cd levels can quickly and accurately identify the high- and low-Cd accumulating rice cultivars.
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Affiliation(s)
- Zhong Tang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Ting-Ting You
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Ya-Fang Li
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Zhi-Xian Tang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Miao-Qing Bao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Ge Dong
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Zhong-Rui Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
| | - Peng Wang
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China; Centre for Agriculture and Health, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Fang-Jie Zhao
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China
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Wu X, Deng H, Wang Q, Lei L, Gao Y, Hao G. Meta-learning shows great potential in plant disease recognition under few available samples. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:767-782. [PMID: 36883481 DOI: 10.1111/tpj.16176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 02/15/2023] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta-learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.
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Affiliation(s)
- Xue Wu
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Hongyu Deng
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Qi Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Liang Lei
- School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 550000, Guangzhou, China
| | - Yangyang Gao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China
| | - Gefei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, Guizhou, China
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31
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Haw YH, Lai KW, Chuah JH, Bejo SK, Husin NA, Hum YC, Yee PL, Tee CATH, Ye X, Wu X. Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods. PeerJ Comput Sci 2023; 9:e1325. [PMID: 37346512 PMCID: PMC10280561 DOI: 10.7717/peerj-cs.1325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/13/2023] [Indexed: 06/23/2023]
Abstract
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.
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Affiliation(s)
- Yu Hong Haw
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siti Khairunniza Bejo
- Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Nur Azuan Husin
- Department of Biological and Agricultural Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, Kajang, Selangor, Malaysia
| | - Por Lip Yee
- Department of Computer System and Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Xin Ye
- YLZ Eaccessy Information Technology Co., Ltd, Xiamen, China
| | - Xiang Wu
- School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
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32
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Amas JC, Thomas WJW, Zhang Y, Edwards D, Batley J. Key Advances in the New Era of Genomics-Assisted Disease Resistance Improvement of Brassica Species. PHYTOPATHOLOGY 2023:PHYTO08220289FI. [PMID: 36324059 DOI: 10.1094/phyto-08-22-0289-fi] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Disease resistance improvement remains a major focus in breeding programs as diseases continue to devastate Brassica production systems due to intensive cultivation and climate change. Genomics has paved the way to understand the complex genomes of Brassicas, which has been pivotal in the dissection of the genetic underpinnings of agronomic traits driving the development of superior cultivars. The new era of genomics-assisted disease resistance breeding has been marked by the development of high-quality genome references, accelerating the identification of disease resistance genes controlling both qualitative (major) gene and quantitative resistance. This facilitates the development of molecular markers for marker assisted selection and enables genome editing approaches for targeted gene manipulation to enhance the genetic value of disease resistance traits. This review summarizes the key advances in the development of genomic resources for Brassica species, focusing on improved genome references, based on long-read sequencing technologies and pangenome assemblies. This is further supported by the advances in pathogen genomics, which have resulted in the discovery of pathogenicity factors, complementing the mining of disease resistance genes in the host. Recognizing the co-evolutionary arms race between the host and pathogen, it is critical to identify novel resistance genes using crop wild relatives and synthetic cultivars or through genetic manipulation via genome-editing to sustain the development of superior cultivars. Integrating these key advances with new breeding techniques and improved phenotyping using advanced data analysis platforms will make disease resistance improvement in Brassica species more efficient and responsive to current and future demands.
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Affiliation(s)
- Junrey C Amas
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6001
| | - William J W Thomas
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6001
| | - Yueqi Zhang
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6001
| | - David Edwards
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6001
| | - Jacqueline Batley
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia 6001
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Abstract
Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.
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Yuan P, Xu S, Zhai Z, Xu H. Research of intelligent reasoning system of Arabidopsis thaliana phenotype based on automated multi-task machine learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1048016. [PMID: 36866380 PMCID: PMC9974140 DOI: 10.3389/fpls.2023.1048016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Traditional machine learning in plant phenotyping research requires the assistance of professional data scientists and domain experts to adjust the structure and hy-perparameters tuning of neural network models with much human intervention, making the model training and deployment ineffective. In this paper, the automated machine learning method is researched to construct a multi-task learning model for Arabidopsis thaliana genotype classification, leaf number, and leaf area regression tasks. The experimental results show that the genotype classification task's accuracy and recall achieved 98.78%, precision reached 98.83%, and classification F 1 value reached 98.79%, as well as the R 2 of leaf number regression task and leaf area regression task reached 0.9925 and 0.9997 respectively. The experimental results demonstrated that the multi-task automated machine learning model can combine the benefits of multi-task learning and automated machine learning, which achieved more bias information from related tasks and improved the overall classification and prediction effect. Additionally, the model can be created automatically and has a high degree of generalization for better phenotype reasoning. In addition, the trained model and system can be deployed on cloud platforms for convenient application.
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Affiliation(s)
- Peisen Yuan
- *Correspondence: Peisen Yuan, ; Zhaoyu Zhai,
| | | | - Zhaoyu Zhai
- *Correspondence: Peisen Yuan, ; Zhaoyu Zhai,
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35
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Aasim M, Akin F, Ali SA, Taskin MB, Colak MS, Khawar KM. Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea ( Cicer arietinum L). PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2023; 29:289-304. [PMID: 36875725 PMCID: PMC9981858 DOI: 10.1007/s12298-023-01282-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/06/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67-100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23-7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91-53.43% for roots, and 14.53-44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43-1.59 (roots) and 0.92-2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high R 2 values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-023-01282-z.
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Affiliation(s)
- Muhammad Aasim
- Department of Plant Protection, Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Fatma Akin
- Department of Molecular Biology and Genetics, Faculty of Science, Necmettin Erbakan University, Konya, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Mehmet Burak Taskin
- Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Ankara University, Ankara, Turkey
| | - Muslume Sevba Colak
- Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara, Turkey
| | - Khalid Mahmood Khawar
- Department of Field Crops, Faculty of Agriculture, Ankara University, Ankara, Turkey
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36
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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37
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Guo T, Li X. Machine learning for predicting phenotype from genotype and environment. Curr Opin Biotechnol 2023; 79:102853. [PMID: 36463837 DOI: 10.1016/j.copbio.2022.102853] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 12/03/2022]
Abstract
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.
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Affiliation(s)
- Tingting Guo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164, USA; Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA.
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Dwivedi SL, Garcia-Oliveira AL, Govindaraj M, Ortiz R. Biofortification to avoid malnutrition in humans in a changing climate: Enhancing micronutrient bioavailability in seed, tuber, and storage roots. FRONTIERS IN PLANT SCIENCE 2023; 14:1119148. [PMID: 36794214 PMCID: PMC9923027 DOI: 10.3389/fpls.2023.1119148] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
Malnutrition results in enormous socio-economic costs to the individual, their community, and the nation's economy. The evidence suggests an overall negative impact of climate change on the agricultural productivity and nutritional quality of food crops. Producing more food with better nutritional quality, which is feasible, should be prioritized in crop improvement programs. Biofortification refers to developing micronutrient -dense cultivars through crossbreeding or genetic engineering. This review provides updates on nutrient acquisition, transport, and storage in plant organs; the cross-talk between macro- and micronutrients transport and signaling; nutrient profiling and spatial and temporal distribution; the putative and functionally characterized genes/single-nucleotide polymorphisms associated with Fe, Zn, and β-carotene; and global efforts to breed nutrient-dense crops and map adoption of such crops globally. This article also includes an overview on the bioavailability, bioaccessibility, and bioactivity of nutrients as well as the molecular basis of nutrient transport and absorption in human. Over 400 minerals (Fe, Zn) and provitamin A-rich cultivars have been released in the Global South. Approximately 4.6 million households currently cultivate Zn-rich rice and wheat, while ~3 million households in sub-Saharan Africa and Latin America benefit from Fe-rich beans, and 2.6 million people in sub-Saharan Africa and Brazil eat provitamin A-rich cassava. Furthermore, nutrient profiles can be improved through genetic engineering in an agronomically acceptable genetic background. The development of "Golden Rice" and provitamin A-rich dessert bananas and subsequent transfer of this trait into locally adapted cultivars are evident, with no significant change in nutritional profile, except for the trait incorporated. A greater understanding of nutrient transport and absorption may lead to the development of diet therapy for the betterment of human health.
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Affiliation(s)
| | - Ana Luísa Garcia-Oliveira
- International Maize and Wheat Research Center, Centro Internacional de Mejoramiento de Maíz. y Trigo (CIMMYT), Nairobi, Kenya
- Department of Molecular Biology, College of Biotechnology, CCS Haryana Agricultural University, Hissar, India
| | - Mahalingam Govindaraj
- HarvestPlus Program, Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Rodomiro Ortiz
- Swedish University of Agricultural Sciences, Lomma, Sweden
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada,*Correspondence: Sheikh Jubair ✉
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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40
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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41
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Fan J, Li Y, Yu S, Gou W, Guo X, Zhao C. Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field. RESEARCH (WASHINGTON, D.C.) 2023; 6:0059. [PMID: 36951796 PMCID: PMC10027232 DOI: 10.34133/research.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/07/2023] [Indexed: 01/22/2023]
Abstract
The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.
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Affiliation(s)
- Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Yinglun Li
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Shuan Yu
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Wenbo Gou
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
- Address correspondence to: (X.G.); (C.Z.)
| | - Chunjiang Zhao
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
- Address correspondence to: (X.G.); (C.Z.)
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42
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Stevens K, Johnston IG, Luna E. Data science approaches provide a roadmap to understanding the role of abscisic acid in defence. QUANTITATIVE PLANT BIOLOGY 2023; 4:e2. [PMID: 37077700 PMCID: PMC10095806 DOI: 10.1017/qpb.2023.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 05/03/2023]
Abstract
Abscisic acid (ABA) is a plant hormone well known to regulate abiotic stress responses. ABA is also recognised for its role in biotic defence, but there is currently a lack of consensus on whether it plays a positive or negative role. Here, we used supervised machine learning to analyse experimental observations on the defensive role of ABA to identify the most influential factors determining disease phenotypes. ABA concentration, plant age and pathogen lifestyle were identified as important modulators of defence behaviour in our computational predictions. We explored these predictions with new experiments in tomato, demonstrating that phenotypes after ABA treatment were indeed highly dependent on plant age and pathogen lifestyle. Integration of these new results into the statistical analysis refined the quantitative model of ABA influence, suggesting a framework for proposing and exploiting further research to make more progress on this complex question. Our approach provides a unifying road map to guide future studies involving the role of ABA in defence.
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Affiliation(s)
- Katie Stevens
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
- Authors for correspondence: K. Stevens, E. Luna, E-mail: ;
| | - Iain G. Johnston
- Department of Mathematics, University of Bergen, Bergen, Norway
- Computational Biology Unit, University of Bergen, Bergen, Norway
| | - Estrella Luna
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
- Authors for correspondence: K. Stevens, E. Luna, E-mail: ;
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43
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Mahmood U, Li X, Fan Y, Chang W, Niu Y, Li J, Qu C, Lu K. Multi-omics revolution to promote plant breeding efficiency. FRONTIERS IN PLANT SCIENCE 2022; 13:1062952. [PMID: 36570904 PMCID: PMC9773847 DOI: 10.3389/fpls.2022.1062952] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.
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Affiliation(s)
- Umer Mahmood
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Xiaodong Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yonghai Fan
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Wei Chang
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Yue Niu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Jiana Li
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Cunmin Qu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
| | - Kun Lu
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, College of Agronomy and Biotechnology, Southwest University, Chongqing, China
- Academy of Agricultural Sciences, Southwest University, Chongqing, China
- Engineering Research Center of South Upland Agriculture, Ministry of Education, Chongqing, China
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44
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Han B, Li Y, Bie Z, Peng C, Huang Y, Xu S. MIX-NET: Deep Learning-Based Point Cloud Processing Method for Segmentation and Occlusion Leaf Restoration of Seedlings. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11233342. [PMID: 36501381 PMCID: PMC9739940 DOI: 10.3390/plants11233342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/19/2022] [Accepted: 11/24/2022] [Indexed: 05/27/2023]
Abstract
In this paper, a novel point cloud segmentation and completion framework is proposed to achieve high-quality leaf area measurement of melon seedlings. In particular, the input of our algorithm is the point cloud data collected by an Azure Kinect camera from the top view of the seedlings, and our method can enhance measurement accuracy from two aspects based on the acquired data. On the one hand, we propose a neighborhood space-constrained method to effectively filter out the hover points and outlier noise of the point cloud, which can enhance the quality of the point cloud data significantly. On the other hand, by leveraging the purely linear mixer mechanism, a new network named MIX-Net is developed to achieve segmentation and completion of the point cloud simultaneously. Different from previous methods that separate these two tasks, the proposed network can better balance these two tasks in a more definite and effective way, leading to satisfactory performance on these two tasks. The experimental results prove that our methods can outperform other competitors and provide more accurate measurement results. Specifically, for the seedling segmentation task, our method can obtain a 3.1% and 1.7% performance gain compared with PointNet++ and DGCNN, respectively. Meanwhile, the R2 of leaf area measurement improved from 0.87 to 0.93 and MSE decreased from 2.64 to 2.26 after leaf shading completion.
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Affiliation(s)
- Binbin Han
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
- School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Yaqin Li
- School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Zhilong Bie
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan 430070, China
| | - Chengli Peng
- Electronic Information School, Wuhan University, Wuhan 430072, China
| | - Yuan Huang
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan 430070, China
| | - Shengyong Xu
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
- Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
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Feng H, Guo C, Li Z, Gao Y, Zhang Q, Geng Z, Wang J, Chen G, Liu K, Li H, Yang W. Machine learning assisted dynamic phenotypes and genomic variants help understand the ecotype divergence in rapeseed. FRONTIERS IN PLANT SCIENCE 2022; 13:1028779. [PMID: 36457523 PMCID: PMC9705987 DOI: 10.3389/fpls.2022.1028779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/14/2022] [Indexed: 06/17/2023]
Abstract
Three ecotypes of rapeseed, winter, spring, and semi-winter, have been formed to enable the plant to adapt to different geographic areas. Although several major loci had been found to contribute to the flowering divergence, the genomic footprints and associated dynamic plant architecture in the vegetative growth stage underlying the ecotype divergence remain largely unknown in rapeseed. Here, a set of 41 dynamic i-traits and 30 growth-related traits were obtained by high-throughput phenotyping of 171 diverse rapeseed accessions. Large phenotypic variation and high broad-sense heritability were observed for these i-traits across all developmental stages. Of these, 19 i-traits were identified to contribute to the divergence of three ecotypes using random forest model of machine learning approach, and could serve as biomarkers to predict the ecotype. Furthermore, we analyzed genomic variations of the population, QTL information of all dynamic i-traits, and genomic basis of the ecotype differentiation. It was found that 213, 237, and 184 QTLs responsible for the differentiated i-traits overlapped with the signals of ecotype divergence between winter and spring, winter and semi-winter, and spring and semi-winter, respectively. Of which, there were four common divergent regions between winter and spring/semi-winter and the strongest divergent regions between spring and semi-winter were found to overlap with the dynamic QTLs responsible for the differentiated i-traits at multiple growth stages. Our study provides important insights into the divergence of plant architecture in the vegetative growth stage among the three ecotypes, which was contributed to by the genetic differentiation, and might contribute to environmental adaption and yield improvement.
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Affiliation(s)
- Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Chaocheng Guo
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zongyi Li
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuan Gao
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zedong Geng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jing Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Guoxing Chen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Haitao Li
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, School of Life Sciences, Hubei University, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
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Mu H, Wang B, Yuan F. Bioinformatics in Plant Breeding and Research on Disease Resistance. PLANTS (BASEL, SWITZERLAND) 2022; 11:3118. [PMID: 36432847 PMCID: PMC9696050 DOI: 10.3390/plants11223118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/04/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.
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An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation. Foods 2022. [PMCID: PMC9601458 DOI: 10.3390/foods11203248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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Naqvi RZ, Siddiqui HA, Mahmood MA, Najeebullah S, Ehsan A, Azhar M, Farooq M, Amin I, Asad S, Mukhtar Z, Mansoor S, Asif M. Smart breeding approaches in post-genomics era for developing climate-resilient food crops. FRONTIERS IN PLANT SCIENCE 2022; 13:972164. [PMID: 36186056 PMCID: PMC9523482 DOI: 10.3389/fpls.2022.972164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.
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Brunetto G, Stefanello LO, Kulmann MSDS, Tassinari A, de Souza ROS, Rozane DE, Tiecher TL, Ceretta CA, Ferreira PAA, de Siqueira GN, Parent LÉ. Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models. PLANTS 2022; 11:plants11182419. [PMID: 36145819 PMCID: PMC9501305 DOI: 10.3390/plants11182419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 12/02/2022]
Abstract
Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in ‘Alicante Bouschet’ vineyards. The 5-year (2015–2019) fertilizer trial comprised six N doses (0–20–40–60–80–100 kg N ha−1) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha−1 and a total anthocyanin content (TAC) of 3900 mg L−1. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg−1 in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha−1 depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.
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Affiliation(s)
- Gustavo Brunetto
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Correspondence: ; Tel.: +55-32208108
| | | | | | - Adriele Tassinari
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | - Danilo Eduardo Rozane
- Fruticulture Department, State University of Paulista “Julio Mesquita Filho”, Registro 11900-000, Brazil
| | - Tadeu Luis Tiecher
- Rio Grande do Sul Federal Institute, Campus Restinga, Porto Alegre 91791-508, Brazil
| | - Carlos Alberto Ceretta
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | | | - Léon Étienne Parent
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Department of Soil and Agri-Food Engineering, Laval University, Québec City, QC G1V 0A6, Canada
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