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Melicherová N, Gloser V, Skalák J, Trtílek M, Lavická J, Foret F. A new device for online nanoscale sampling and capillary electrophoresis analysis of plant sap composition. Electrophoresis 2024; 45:310-317. [PMID: 37880866 DOI: 10.1002/elps.202300201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 10/27/2023]
Abstract
In this work, an online sampling of plant xylem sap combined with an efficient (CE)-based method was developed and applied to study the kinetics of changes in the sap composition and to assess plant fitness under stress conditions comprehensively. A laboratory-built CE device was developed to provide online sampling and CE analysis of various ionogenic species in the sap during plant stress response. The rapid online sampling and short CE analysis time allow for real-time monitoring of changes in sap constituents in the living plant during the stress response. The developed device was successfully used to analyze chloride, nitrate, and sulfate ions in the plant xylem during the salt stress or stress caused by nitrate deficiency within short time scales.
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Affiliation(s)
- Natália Melicherová
- Department of Bioanalytical Instrumentation, Institute of Analytical Chemistry of the Czech Academy of Sciences, Brno, Czech Republic
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Vít Gloser
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jan Skalák
- Functional Genomics and Proteomics of Plants, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Trtílek
- PSI (Photon Systems Instruments), spol. s.r.o., Průmyslová, Drásov, Czech Republic
| | - Jana Lavická
- Department of Bioanalytical Instrumentation, Institute of Analytical Chemistry of the Czech Academy of Sciences, Brno, Czech Republic
| | - František Foret
- Department of Bioanalytical Instrumentation, Institute of Analytical Chemistry of the Czech Academy of Sciences, Brno, Czech Republic
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2
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Jurado-Ruiz F, Nguyen TP, Peller J, Aranzana MJ, Polder G, Aarts MGM. LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union. Plant Methods 2024; 20:11. [PMID: 38233879 PMCID: PMC10795293 DOI: 10.1186/s13007-024-01138-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND The study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic. RESULTS We tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background. CONCLUSION The method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.
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Affiliation(s)
- Federico Jurado-Ruiz
- Center for Research in Agricultural Genomics (CRAG), Cerdanyola, 08193, Barcelona, Spain
| | - Thu-Phuong Nguyen
- Laboratory of Genetics, Wageningen University and Research (WUR), Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Joseph Peller
- Greenhouse Horticulture, Wageningen University and Research (WUR), Wageningen, The Netherlands
| | - María José Aranzana
- Center for Research in Agricultural Genomics (CRAG), Cerdanyola, 08193, Barcelona, Spain
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Barcelona, Spain
| | - Gerrit Polder
- Greenhouse Horticulture, Wageningen University and Research (WUR), Wageningen, The Netherlands
| | - Mark G M Aarts
- Laboratory of Genetics, Wageningen University and Research (WUR), Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.
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3
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Liu J, Zhu Y, Song L, Su X, Li J, Zheng J, Zhu X, Ren L, Wang W, Li X. Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery. Front Plant Sci 2023; 14:1284235. [PMID: 38192693 PMCID: PMC10773816 DOI: 10.3389/fpls.2023.1284235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.
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Affiliation(s)
- Jikai Liu
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
- Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Yongji Zhu
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Lijuan Song
- Institute of Agricultural Remote Sensing and Information, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China
- School of Management, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China
| | - Xiangxiang Su
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Jun Li
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Jing Zheng
- College of Life Science, Langfang Normal University, Langfang, Hebei, China
| | - Xueqing Zhu
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Lantian Ren
- Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
- College of Agriculture, Anhui Science and Technology University, Chuzhou, Anhui, China
| | - Wenhui Wang
- College of Life Science, Langfang Normal University, Langfang, Hebei, China
| | - Xinwei Li
- College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China
- Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China
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Jurado-Ruiz F, Rousseau D, Botía JA, Aranzana MJ. GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers. Plant Phenomics 2023; 5:0113. [PMID: 38239740 PMCID: PMC10795539 DOI: 10.34133/plantphenomics.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/23/2023] [Indexed: 01/22/2024]
Abstract
Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks (NNs), particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, an NN model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs. Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images, consistent with biological interpretation. While using substantial SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and dataset balance in terms of phenotypes for more precise outcomes.
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Affiliation(s)
- Federico Jurado-Ruiz
- Center for Research in Agricultural Genomics (CRAG), 08193 Barcelona, Cerdanyola, Spain
| | - David Rousseau
- Université d’Angers, LARIS, INRAe UMR IRHS, 49000 Angers, France
| | - Juan A. Botía
- Department of Information and Communication Engineering,
University of Murcia, 30071 Murcia, Spain
| | - Maria José Aranzana
- Center for Research in Agricultural Genomics (CRAG), 08193 Barcelona, Cerdanyola, Spain
- IRTA (Institut de Recerca i Tecnologia Agroalimentàries), Barcelona, Spain
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Anshori MF, Dirpan A, Sitaresmi T, Rossi R, Farid M, Hairmansis A, Sapta Purwoko B, Suwarno WB, Nugraha Y. An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review. Heliyon 2023; 9:e21650. [PMID: 38027954 PMCID: PMC10660044 DOI: 10.1016/j.heliyon.2023.e21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
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Affiliation(s)
| | - Andi Dirpan
- Department of Agricultural Technology, Hasanuddin University, Makassar, 90245, Indonesia
- Center of Excellence in Science and Technology on Food Product Diversification, 90245, Makassar, Indonesia
| | - Trias Sitaresmi
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Riccardo Rossi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Muh Farid
- Department of Agronomy, Hasanuddin University, Makassar, 90245, Indonesia
| | - Aris Hairmansis
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Bambang Sapta Purwoko
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Yudhistira Nugraha
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
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Gao L, Kantar MB, Moxley D, Ortiz-Barrientos D, Rieseberg LH. Crop adaptation to climate change: An evolutionary perspective. Mol Plant 2023; 16:1518-1546. [PMID: 37515323 DOI: 10.1016/j.molp.2023.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 07/30/2023]
Abstract
The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.
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Affiliation(s)
- Lexuan Gao
- CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Michael B Kantar
- Department of Tropical Plant & Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Dylan Moxley
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Daniel Ortiz-Barrientos
- School of Biological Sciences and Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Loren H Rieseberg
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada.
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7
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. Plants (Basel) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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8
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Dossa EN, Shimelis H, Mrema E, Shayanowako ATI, Laing M. Genetic resources and breeding of maize for Striga resistance: a review. Front Plant Sci 2023; 14:1163785. [PMID: 37235028 PMCID: PMC10206272 DOI: 10.3389/fpls.2023.1163785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 04/07/2023] [Indexed: 05/28/2023]
Abstract
The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.
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Affiliation(s)
- Emeline Nanou Dossa
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Hussein Shimelis
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Emmanuel Mrema
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
- Tanzania Agricultural Research Institute, Tumbi Center, Tabora, Tanzania
| | | | - Mark Laing
- School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa
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Anand A, Subramanian M, Kar D. Breeding techniques to dispense higher genetic gains. Front Plant Sci 2023; 13:1076094. [PMID: 36743551 PMCID: PMC9893280 DOI: 10.3389/fpls.2022.1076094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/28/2022] [Indexed: 06/18/2023]
Abstract
Plant breeding techniques encompass all the processes aimed at improving the genetic characteristics of a crop. It helps in achieving desirable characteristics like resistance to diseases and pests, tolerance to environmental stresses, higher yield and improved quality of the crop. This review article aims to describe and evaluate the current plant breeding techniques and novel methods. This qualitative review employs a comparative approach in exploring the different plant breeding techniques. Conventional plant breeding techniques were compared with modern ones to understand the advancements in plant biotechnology. Backcross breeding, mass selection, and pure-line selection were all discussed in conventional plant breeding for self-pollination and recurrent selection and hybridisation were employed for cross-pollinated crops. Modern techniques comprise of CRISPR Cas-9, high-throughput phenotyping, marker-assisted selection and genomic selection. Further, novel techniques were reviewed to gain more insight. An in-depth analysis of conventional and modern plant breeding has helped gain insight on the advantages and disadvantages of the two. Modern breeding techniques have an upper hand as they are more reliable and less time consuming. It is also more accurate as it is a genotype-based method. However, conventional breeding techniques are cost effective and require less expertise. Modern plant breeding has an upper hand as it uses genomics techniques. Techniques like QTL mapping, marker assisted breeding aid in selection of superior plants right at the seedling stage, which is impossible with conventional breeding. Unlike the conventional method, modern methods are capable of selecting recessive alleles by using different markers. Modern plant breeding is a science and therefore more reliable and accurate.
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Jeon D, Kang Y, Lee S, Choi S, Sung Y, Lee TH, Kim C. Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. Front Plant Sci 2023; 14:1092584. [PMID: 36743488 PMCID: PMC9892199 DOI: 10.3389/fpls.2023.1092584] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
As the world's population grows and food needs diversification, the demand for cereals and horticultural crops with beneficial traits increases. In order to meet a variety of demands, suitable cultivars and innovative breeding methods need to be developed. Breeding methods have changed over time following the advance of genetics. With the advent of new sequencing technology in the early 21st century, predictive breeding, such as genomic selection (GS), emerged when large-scale genomic information became available. GS shows good predictive ability for the selection of individuals with traits of interest even for quantitative traits by using various types of the whole genome-scanning markers, breaking away from the limitations of marker-assisted selection (MAS). In the current review, we briefly describe the history of breeding techniques, each breeding method, various statistical models applied to GS and methods to increase the GS efficiency. Consequently, we intend to propose and define the term digital breeding through this review article. Digital breeding is to develop a predictive breeding methods such as GS at a higher level, aiming to minimize human intervention by automatically proceeding breeding design, propagating breeding populations, and to make selections in consideration of various environments, climates, and topography during the breeding process. We also classified the phases of digital breeding based on the technologies and methods applied to each phase. This review paper will provide an understanding and a direction for the final evolution of plant breeding in the future.
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Affiliation(s)
- Donghyun Jeon
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Yuna Kang
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Solji Lee
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Sehyun Choi
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Yeonjun Sung
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
| | - Tae-Ho Lee
- Genomics Division, National Institute of Agricultural Sciences, Jeonju, Republic of Korea
| | - Changsoo Kim
- Plant Computational Genomics Laboratory, Department of Science in Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
- Plant Computational Genomics Laboratory, Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
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Poggi GM, Corneti S, Aloisi I, Ventura F. Environment-oriented selection criteria to overcome controversies in breeding for drought resistance in wheat. J Plant Physiol 2023; 280:153895. [PMID: 36529076 DOI: 10.1016/j.jplph.2022.153895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/17/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Wheat is one of the most important cereal crops, representing a fundamental source of calories and protein for the global human population. Drought stress (DS) is a widespread phenomenon, already affecting large wheat-growing areas worldwide, and a major threat for cereal productivity, resulting in consistent losses in average grain yield (GY). Climate change is projected to exacerbate DS incidence and severity by increasing temperatures and changing rainfall patterns. Estimating that wheat production has to substantially increase to guarantee food security to a demographically expanding human population, the need for breeding programs focused on improving wheat drought resistance is manifest. Drought occurrence, in terms of time of appearance, duration, frequency, and severity, along the plant's life cycle varies significantly among different environments and different agricultural years, making it difficult to identify reliable phenological, morphological, and functional traits to be used as effective breeding tools. The situation is further complicated by the presence of confounding factors, e.g., other concomitant abiotic stresses, in an open-field context. Consequently, the relationship between morpho-functional traits and GY under water deficit is often contradictory; moreover, controversies have emerged not only on which traits are to be preferred, but also on how one specific trait should be desired. In this review, we attempt to identify the possible causes of these disputes and propose the most suitable selection criteria in different target environments and, thus, the best trait combinations for breeders in different drought contexts. In fact, an environment-oriented approach could be a valuable solution to overcome controversies in identifying the proper selection criteria for improving wheat drought resistance.
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Affiliation(s)
- Giovanni Maria Poggi
- Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum, University of Bologna, Bologna, Italy; Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Simona Corneti
- Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Iris Aloisi
- Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum, University of Bologna, Bologna, Italy.
| | - Francesca Ventura
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum, University of Bologna, Bologna, Italy
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12
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Ramakrishnan M, Zhang Z, Mullasseri S, Kalendar R, Ahmad Z, Sharma A, Liu G, Zhou M, Wei Q. Epigenetic stress memory: A new approach to study cold and heat stress responses in plants. Front Plant Sci 2022; 13:1075279. [PMID: 36570899 PMCID: PMC9772030 DOI: 10.3389/fpls.2022.1075279] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/23/2022] [Indexed: 05/28/2023]
Abstract
Understanding plant stress memory under extreme temperatures such as cold and heat could contribute to plant development. Plants employ different types of stress memories, such as somatic, intergenerational and transgenerational, regulated by epigenetic changes such as DNA and histone modifications and microRNAs (miRNA), playing a key role in gene regulation from early development to maturity. In most cases, cold and heat stresses result in short-term epigenetic modifications that can return to baseline modification levels after stress cessation. Nevertheless, some of the modifications may be stable and passed on as stress memory, potentially allowing them to be inherited across generations, whereas some of the modifications are reactivated during sexual reproduction or embryogenesis. Several stress-related genes are involved in stress memory inheritance by turning on and off transcription profiles and epigenetic changes. Vernalization is the best example of somatic stress memory. Changes in the chromatin structure of the Flowering Locus C (FLC) gene, a MADS-box transcription factor (TF), maintain cold stress memory during mitosis. FLC expression suppresses flowering at high levels during winter; and during vernalization, B3 TFs, cold memory cis-acting element and polycomb repressive complex 1 and 2 (PRC1 and 2) silence FLC activation. In contrast, the repression of SQUAMOSA promoter-binding protein-like (SPL) TF and the activation of Heat Shock TF (HSFA2) are required for heat stress memory. However, it is still unclear how stress memory is inherited by offspring, and the integrated view of the regulatory mechanisms of stress memory and mitotic and meiotic heritable changes in plants is still scarce. Thus, in this review, we focus on the epigenetic regulation of stress memory and discuss the application of new technologies in developing epigenetic modifications to improve stress memory.
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Affiliation(s)
- Muthusamy Ramakrishnan
- Co-Innovation Center for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Zhijun Zhang
- Bamboo Industry Institute, Zhejiang A&F University, Hangzhou, Zhejiang, China
- School of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, Zhejiang, China
| | - Sileesh Mullasseri
- Department of Zoology, St. Albert’s College (Autonomous), Kochi, Kerala, India
| | - Ruslan Kalendar
- Helsinki Institute of Life Science HiLIFE, Biocenter 3, University of Helsinki, Helsinki, Finland
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | - Zishan Ahmad
- Co-Innovation Center for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Anket Sharma
- State Key Laboratory of Subtropical Silviculture, Bamboo Industry Institute, Zhejiang A&F University, Hangzhou, Zhejiang, China
| | - Guohua Liu
- Co-Innovation Center for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Mingbing Zhou
- State Key Laboratory of Subtropical Silviculture, Bamboo Industry Institute, Zhejiang A&F University, Hangzhou, Zhejiang, China
- Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-Efficiency Utilization, Zhejiang A&F University, Hangzhou, Zhejiang, China
| | - Qiang Wei
- Co-Innovation Center for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. Plant Commun 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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Savvides AM, Velez‐Ramirez AI, Fotopoulos V. Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration. Physiol Plant 2022; 174:e13762. [PMID: 36281841 PMCID: PMC9542539 DOI: 10.1111/ppl.13762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 05/31/2023]
Abstract
Water stress may greatly limit plant functionality and growth. Stomatal closure and consequently reduced transpiration are considered as early and sensitive plant responses to drought and salinity stress. An important consequence of stomatal closure under water stress is the rise of leaf temperature (Tleaf ), yet Tleaf is not only fluctuating with stomatal closure. It is regulated by several plant parameters and environmental factors. Thermal imaging and different stress indices, incorporating actual leaf/crop temperature and reference temperatures, were developed in previous studies toward normalizing for effects unassociated to water stress on Tleaf , aiming at a more efficient water stress assessment. The concept of stress indices has not been extensively studied on the model plant Arabidopsis thaliana. Therefore, the aim of this study was to examine the different indices employed in previous studies in assessing rosette transpiration rate (E) in Arabidopsis plants grown under two different light environments and subjected to salinity. After salinity imposition, E was gravimetrically quantified, and thermal imaging was employed to quantify rosette (Trosette ) and artificial reference temperature (Twet, Tdry ). Trosette and several water stress indices were tested for their relation to E. Among the microclimatic growth conditions tested, RWSI1 ([Trosette - Twet ]/[Tdry - Twet ]) and RWSI2 ([Tdry - Trosette ]/[Tdry - Twet ]) were well linearly-related to E, irrespective of the light environment, while the sole use of either Twet or Tdry in different combinations with Trosette returned less accurate results. This study provides evidence that selected combinations of Trosette , Tdry , and Twet can be utilized to assess E under water stress irrespective of the light environment.
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Affiliation(s)
- Andreas M. Savvides
- Department of Agricultural Sciences, Biotechnology and Food ScienceCyprus University of TechnologyLimassolCyprus
| | - Aaron I. Velez‐Ramirez
- Laboratorio de Ciencias Agrogenómicas, Escuela Nacional de Estudios Superiores Unidad LeónUniversidad Nacional Autónoma de MéxicoLeónMexico
- Laboratorio Nacional PlanTECC, Escuela Nacional de Estudios Superiores Unidad LeónUniversidad Nacional Autónoma de MéxicoLeónMexico
| | - Vasileios Fotopoulos
- Department of Agricultural Sciences, Biotechnology and Food ScienceCyprus University of TechnologyLimassolCyprus
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Li H, Shi H, Du A, Mao Y, Fan K, Wang Y, Shen Y, Wang S, Xu X, Tian L, Wang H, Ding Z. Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet. Front Plant Sci 2022; 13:922797. [PMID: 35937317 PMCID: PMC9355617 DOI: 10.3389/fpls.2022.922797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
Brown blight, target spot, and tea coal diseases are three major leaf diseases of tea plants, and Apolygus lucorum is a major pest in tea plantations. The traditional symptom recognition of tea leaf diseases and insect pests is mainly through manual identification, which has some problems, such as low accuracy, low efficiency, strong subjectivity, and so on. Therefore, it is very necessary to find a method that could effectively identify tea plants diseases and pests. In this study, we proposed a recognition framework of tea leaf disease and insect pest symptoms based on Mask R-CNN, wavelet transform and F-RNet. First, Mask R-CNN model was used to segment disease spots and insect spots from tea leaves. Second, the two-dimensional discrete wavelet transform was used to enhance the features of the disease spots and insect spots images, so as to obtain the images with four frequencies. Finally, the images of four frequencies were simultaneously input into the four-channeled residual network (F-RNet) to identify symptoms of tea leaf diseases and insect pests. The results showed that Mask R-CNN model could detect 98.7% of DSIS, which ensure that almost disease spots and insect spots can be extracted from leaves. The accuracy of F-RNet model is 88%, which is higher than that of the other models (like SVM, AlexNet, VGG16 and ResNet18). Therefore, this experimental framework can accurately segment and identify diseases and insect spots of tea leaves, which not only of great significance for the accurate identification of tea plant diseases and insect pests, but also of great value for further using artificial intelligence to carry out the comprehensive control of tea plant diseases and insect pests.
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Affiliation(s)
- He Li
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Hongtao Shi
- School of Science and Information Science, Qingdao Agricultural University, Qingdao, China
| | - Anghong Du
- School of Science and Information Science, Qingdao Agricultural University, Qingdao, China
| | - Yilin Mao
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Yaozong Shen
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
| | - Shuangshuang Wang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Xiuxiu Xu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Lili Tian
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Hui Wang
- Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao, China
| | - Zhaotang Ding
- Tea Research Institute, Qingdao Agricultural University, Qingdao, China
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
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16
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Fan Z, Sun N, Qiu Q, Li T, Feng Q, Zhao C. In Situ Measuring Stem Diameters of Maize Crops with a High-Throughput Phenotyping Robot. Remote Sensing 2022; 14:1030. [DOI: 10.3390/rs14041030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Robotic High-Throughput Phenotyping (HTP) technology has been a powerful tool for selecting high-quality crop varieties among large quantities of traits. Due to the advantages of multi-view observation and high accuracy, ground HTP robots have been widely studied in recent years. In this paper, we study an ultra-narrow wheeled robot equipped with RGB-D cameras for inter-row maize HTP. The challenges of the narrow operating space, intensive light changes, and messy cross-leaf interference in rows of maize crops are considered. An in situ and inter-row stem diameter measurement method for HTP robots is proposed. To this end, we first introduce the stem diameter measurement pipeline, in which a convolutional neural network is employed to detect stems, and the point cloud is analyzed to estimate the stem diameters. Second, we present a clustering strategy based on DBSCAN for extracting stem point clouds under the condition that the stem is shaded by dense leaves. Third, we present a point cloud filling strategy to fill the stem region with missing depth values due to the occlusion by other organs. Finally, we employ convex hull and plane projection of the point cloud to estimate the stem diameters. The results show that the R2 and RMSE of stem diameter measurement are up to 0.72 and 2.95 mm, demonstrating its effectiveness.
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Lu X, Li C, Xu W, Wu Y, Wang J, Chen S, Zhang H, Huang H, Huang H, Liu W. Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment. Front Oncol 2021; 11:676124. [PMID: 34557404 PMCID: PMC8454269 DOI: 10.3389/fonc.2021.676124] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/02/2021] [Indexed: 12/23/2022] Open
Abstract
The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.
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Affiliation(s)
- Xiuqin Lu
- Department of Nursing and Health Management, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Chuanyu Li
- Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China
| | - Wenhao Xu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical University, Fudan University, Shanghai, China
| | - Yuanyuan Wu
- Department of Gastroenterology, Naval Medical Center of People's Liberation Army (PLA) of China, Naval Military Medical University, Shanghai, China
| | - Jian Wang
- Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuxian Chen
- Department of Oncology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hailiang Zhang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai Medical University, Fudan University, Shanghai, China
| | - Huadong Huang
- Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China
| | - Haineng Huang
- Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China
| | - Wangrui Liu
- Department of Nursing and Health Management, Shanghai University of Medicine & Health Sciences, Shanghai, China.,Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, Guangxi, China
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