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Keller B, Jung M, Bühlmann-Schütz S, Hodel M, Studer B, Broggini GAL, Patocchi A. The genetic basis of apple shape and size unraveled by digital phenotyping. G3 (BETHESDA, MD.) 2024; 14:jkae045. [PMID: 38441135 PMCID: PMC11075547 DOI: 10.1093/g3journal/jkae045] [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: 01/23/2024] [Accepted: 02/22/2024] [Indexed: 05/08/2024]
Abstract
Great diversity of shape, size, and skin color is observed among the fruits of different apple genotypes. These traits are critical for consumers and therefore interesting targets for breeding new apple varieties. However, they are difficult to phenotype and their genetic basis, especially for fruit shape and ground color, is largely unknown. We used the FruitPhenoBox to digitally phenotype 525 genotypes of the apple reference population (apple REFPOP) genotyped for 303,148 single nucleotide polymorphism (SNP) markers. From the apple images, 573 highly heritable features describing fruit shape and size as well as 17 highly heritable features for fruit skin color were extracted to explore genotype-phenotype relationships. Out of these features, seven principal components (PCs) and 16 features with the Pearson's correlation r < 0.75 (selected features) were chosen to carry out genome-wide association studies (GWAS) for fruit shape and size. Four PCs and eight selected features were used in GWAS for fruit skin color. In total, 69 SNPs scattered over all 17 apple chromosomes were significantly associated with round, conical, cylindrical, or symmetric fruit shapes and fruit size. Novel associations with major effect on round or conical fruit shapes and fruit size were identified on chromosomes 1 and 2. Additionally, 16 SNPs associated with PCs and selected features related to red overcolor as well as green and yellow ground color were found on eight chromosomes. The identified associations can be used to advance marker-assisted selection in apple fruit breeding to systematically select for desired fruit appearance.
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Affiliation(s)
- Beat Keller
- Division of Plant Breeding, Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil 8820, Switzerland
| | - Michaela Jung
- Division of Plant Breeding, Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil 8820, Switzerland
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, Zurich 8092, Switzerland
| | - Simone Bühlmann-Schütz
- Division of Plant Breeding, Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil 8820, Switzerland
| | - Marius Hodel
- Division of Plant Breeding, Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil 8820, Switzerland
| | - Bruno Studer
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, Zurich 8092, Switzerland
| | - Giovanni A L Broggini
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Universitaetstrasse 2, Zurich 8092, Switzerland
| | - Andrea Patocchi
- Division of Plant Breeding, Agroscope, Mueller-Thurgau-Strasse 29, Waedenswil 8820, Switzerland
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2
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Gao W, Yang X, Cao L, Cao F, Liu H, Qiu Q, Shen M, Yu P, Liu Y, Shen X. Screening of Ginkgo Individuals with Superior Growth Structural Characteristics in Different Genetic Groups Using Terrestrial Laser Scanning (TLS) Data. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0092. [PMID: 37745912 PMCID: PMC10515975 DOI: 10.34133/plantphenomics.0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023]
Abstract
With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.
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Affiliation(s)
- Wen Gao
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Xiaoming Yang
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Lin Cao
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Fuliang Cao
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Hao Liu
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Quan Qiu
- College of Forestry and Landscape Architecture,
South China Agricultural University, Guangzhou, Guangdong 510642, PR China
| | - Meng Shen
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
| | - Pengfei Yu
- Suining County Runqi Investment Co. Ltd., Xuzhou, Jiangsu 221200, PR China
| | - Yuhua Liu
- Jiangsu Vocational College of Agriculture and Forestry, Zhenjiang, Jiangsu 212400, PR China
| | - Xin Shen
- Co-Innovation Center for Sustainable Forestry in Southern China,
Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
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3
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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4
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Wu Y, Al-Jumaili SJ, Al-Jumeily D, Bian H. Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228626. [PMID: 36433222 PMCID: PMC9695716 DOI: 10.3390/s22228626] [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: 08/05/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 05/10/2023]
Abstract
This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.
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Affiliation(s)
- Yawen Wu
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
| | - Saba J. Al-Jumaili
- Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
| | - Dhiya Al-Jumeily
- School of Computer Science and Mathematics, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 5UX, UK
| | - Haiyi Bian
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
- Correspondence: ; Tel.: +86-17601449848
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Agulheiro-Santos AC, Ricardo-Rodrigues S, Laranjo M, Melgão C, Velázquez R. Non-destructive prediction of total soluble solids in strawberry using near infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:4866-4872. [PMID: 35244203 DOI: 10.1002/jsfa.11849] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/02/2022] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Near-infrared spectroscopy (NIRS) is considered to be a fast and reliable non-destructive technique for fruit analysis. Considering that consumers are looking for strawberries with good sweetness, texture, and appearance, producers need to effectively measure the ripeness stage of strawberries to guarantee their final quality. Therefore, the use of this technique can contribute to decreasing the high level of waste and delivering good ripe strawberries to consumers. The present study aimed to evaluate the predictive capacity of NIRS technology, as a possible alternative to conventional methodology, for the analysis of the main organoleptic parameters of strawberries (Fragaria × ananassa Duch.). RESULTS Spectroscopic measurements and physicochemical analyses [total soluble solids (TSS), titratable acidity, colour, texture] of 'Victory' strawberries were carried out. The predictive models developed for titratable acidity, colour and texture were not good enough to quantify those parameters. By contrast, in the NIRS quantitative prediction analysis of TSS, it was observed that the spectral pre-treatment with the highest predictive capacity was the first derivative 1-5-5. The coefficients of determination were: 0.9277 for the calibration model; 0.5755 for the validation model; and 0.8207 for the prediction model, using a seven-factor partial least squares multivariate regression analysis. CONCLUSION Therefore, these results demonstrate that NIR analysis could be used to predict the TSS in strawberry, and further work on sampling is desirable to improve the prediction obtained in the present study. It is shown that NIRS technology is a suitable tool for determining quality attributes of strawberry in a fast, economic, and environmentally friendly way. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Ana Cristina Agulheiro-Santos
- MED - Mediterranean Institute for Agriculture, Environment and Development & CHANGE - Global Change and Sustainability Institute, Institute for Advanced Studies and Research Universidade de Évora, Évora, Portugal
| | - Sara Ricardo-Rodrigues
- MED - Mediterranean Institute for Agriculture, Environment and Development & CHANGE - Global Change and Sustainability Institute, Institute for Advanced Studies and Research Universidade de Évora, Évora, Portugal
| | - Marta Laranjo
- MED - Mediterranean Institute for Agriculture, Environment and Development & CHANGE - Global Change and Sustainability Institute, Institute for Advanced Studies and Research Universidade de Évora, Évora, Portugal
| | - Catarina Melgão
- MED - Mediterranean Institute for Agriculture, Environment and Development & CHANGE - Global Change and Sustainability Institute, Institute for Advanced Studies and Research Universidade de Évora, Évora, Portugal
| | - Rocío Velázquez
- Investigación Aplicada en Hortofruticultura y Jardinería, Instituto Universitario de Recursos Agrarios (INURA), Escuela de Ingeniería Agrarias, Universidad de Extremadura, Badajoz, Spain
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6
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Zhang Y, Zhao D, Liu H, Huang X, Deng J, Jia R, He X, Tahir MN, Lan Y. Research hotspots and frontiers in agricultural multispectral technology: Bibliometrics and scientometrics analysis of the Web of Science. FRONTIERS IN PLANT SCIENCE 2022; 13:955340. [PMID: 36035687 PMCID: PMC9404299 DOI: 10.3389/fpls.2022.955340] [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: 05/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Multispectral technology has a wide range of applications in agriculture. By obtaining spectral information during crop production, key information such as growth, pests and diseases, fertilizer and pesticide application can be determined quickly, accurately and efficiently. The scientific analysis based on Web of Science aims to understand the research hotspots and areas of interest in the field of agricultural multispectral technology. The publications related to agricultural multispectral research in agriculture between 2002 and 2021 were selected as the research objects. The softwares of CiteSpace, VOSviewer, and Microsoft Excel were used to provide a comprehensive review of agricultural multispectral research in terms of research areas, institutions, influential journals, and core authors. Results of the analysis show that the number of publications increased each year, with the largest increase in 2019. Remote sensing, imaging technology, environmental science, and ecology are the most popular research directions. The journal Remote Sensing is one of the most popular publishers, showing a high publishing potential in multispectral research in agriculture. The institution with the most research literature and citations is the USDA. In terms of the number of papers, Mtanga is the author with the most published articles in recent years. Through keyword co-citation analysis, it is determined that the main research areas of this topic focus on remote sensing, crop classification, plant phenotypes and other research areas. The literature co-citation analysis indicates that the main research directions concentrate in vegetation index, satellite remote sensing applications and machine learning modeling. There is still a lot of room for development of multi-spectrum technology. Further development can be carried out in the areas of multi-device synergy, spectral fusion, airborne equipment improvement, and real-time image processing technology, which will cooperate with each other to further play the role of multi-spectrum in agriculture and promote the development of agriculture.
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Affiliation(s)
- Yali Zhang
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Dehua Zhao
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Hanchao Liu
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Xinrong Huang
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Jizhong Deng
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Ruichang Jia
- College of Engineering, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Xiaoping He
- Department of Information Consulting, Library, South China Agricultural University, Guangzhou, China
| | - Muhammad Naveed Tahir
- Department of Agronomy, Pir Mehr Ali Shah-Arid Agriculture University, Rawalpindi, Pakistan
| | - Yubin Lan
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
- College of Electronic Engineering and College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
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7
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Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12030997] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.
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8
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Zheng S, Zhou C, Jiang X, Huang J, Xu D. Progress on Infrared Imaging Technology in Animal Production: A Review. SENSORS 2022; 22:s22030705. [PMID: 35161450 PMCID: PMC8839879 DOI: 10.3390/s22030705] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/28/2021] [Accepted: 01/13/2022] [Indexed: 02/01/2023]
Abstract
Infrared thermography (IRT) imaging technology, as a convenient, efficient, and contactless temperature measurement technology, has been widely applied to animal production. In this review, we systematically summarized the principles and influencing parameters of IRT imaging technology. In addition, we also summed up recent advances of IRT imaging technology in monitoring the temperature of animal surfaces and core anatomical areas, diagnosing early disease and inflammation, monitoring animal stress levels, identifying estrus and ovulation, and diagnosing pregnancy and animal welfare. Finally, we made prospective forecast for future research directions, offering more theoretical references for related research in this field.
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Affiliation(s)
- Shuailong Zheng
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Changfan Zhou
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xunping Jiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jingshu Huang
- Agricultural Development Center of Hubei Province, Wuhan 430064, China;
| | - Dequan Xu
- Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Huazhong Agricultural University, Wuhan 430070, China; (S.Z.); (C.Z.)
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China;
- Colleges of Animal Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence:
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9
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Estimating Productivity, Detecting Biotic Disturbances, and Assessing the Health State of Traditional Olive Groves, Using Nondestructive Phenotypic Techniques. SUSTAINABILITY 2021. [DOI: 10.3390/su14010391] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Conservation of traditional olive groves through effective monitoring of their health state is crucial both at a tree and at a population level. In this study, we introduce a comprehensive methodological framework for estimating the traditional olive grove health state, by considering the fundamental phenotypic, spectral, and thermal traits of the olive trees. We obtained phenotypic information from olive trees on the Greek island of Lesvos by combining this with in situ measurement of spectral reflectance and thermal indices to investigate the effect of the olive tree traits on productivity, the presence of the olive leaf spot disease (OLS), and olive tree classification based on their health state. In this context, we identified a suite of important features, derived from linear and logistic regression models, which can explain productivity and accurately evaluate infected and noninfected trees. The results indicated that either specific traits or combinations of them are statistically significant predictors of productivity, while the occurrence of OLS symptoms can be identified by both the olives’ vitality traits and by the thermal variables. Finally, the classification of olive trees into different health states possibly offers significant information to explain traditional olive grove dynamics for their sustainable management.
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10
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Mantz GM, Rossi FR, Viretto PE, Noelting MC, Maiale SJ. Stem canker caused by Phomopsis spp. Induces changes in polyamine levels and chlorophyll fluorescence parameters in pecan leaves. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2021; 166:761-769. [PMID: 34217132 DOI: 10.1016/j.plaphy.2021.06.050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/18/2021] [Accepted: 06/27/2021] [Indexed: 06/13/2023]
Abstract
Pecan plants are attacked by the fungus Phomopsis spp. that causes stem canker, a serious and emerging disease in commercial orchards. Stem canker, which has been reported in several countries, negatively affects tree canopy health, eventually leading to production losses. The purpose of this study was to inquire into the physiology of pecan plants under stem canker attack by Phomopsis spp. To this end, pecan plants were inoculated with an isolate of Phomopsis spp. and several parameters, such as polyamines, proline, sugars, starch, chlorophyll fluorescence and canopy temperature were analysed. Under artificial inoculation, a high disease incidence was observed with symptoms similar to those in plants showing stem canker under field conditions. Furthermore, the infected stem showed dead tissue with brown necrotic discolouration in the xylem tissue. The free polyamines putrescine, spermidine, and spermine were detected and their levels decreased as leaves aged in the infected plants with respect to the controls. Chlorophyll fluorescence parameters, such as Sm, ψEO, and QbRC decreased under plant infection and therefore the K-band increased. Canopy temperature and proline content increased in the infected plants with respect to the controls while sugar content decreased. These data suggest that stem canker caused by Phomopsis spp. induces physiological changes that are similar to those observed in plants under drought stress. To our knowledge, this is the first study that documents the physiological and biochemical effects derived from pecan-Phomopsis interaction.
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Affiliation(s)
- Guillermo Martin Mantz
- Instituto Tecnológico de Chascomús (INTECH), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-Universidad Nacional de San Martín (UNSAM), Int. Marino Km 8, Chascomús, Provincia de Buenos Aires, Argentina
| | - Franco Ruben Rossi
- Instituto Tecnológico de Chascomús (INTECH), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-Universidad Nacional de San Martín (UNSAM), Int. Marino Km 8, Chascomús, Provincia de Buenos Aires, Argentina
| | - Pablo Esteban Viretto
- Estación Experimental Agropecuaria Valle Inferior del Río Negro (EEA)-Instituto Nacional de Tecnología Agropecuaria (INTA), Valle inferior Río Negro, RN 3 Km 971, Pcia. RN, Argentina
| | - María Cristina Noelting
- Instituto Fitotécnico de Santa Catalina (IFSC), Universidad Nacional de La Plata (UNLP), Garibaldi, 3400, Lavallol, Provincia de Buenos Aires, Argentina
| | - Santiago Javier Maiale
- Instituto Tecnológico de Chascomús (INTECH), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-Universidad Nacional de San Martín (UNSAM), Int. Marino Km 8, Chascomús, Provincia de Buenos Aires, Argentina.
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11
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Penzel M, Herppich WB, Weltzien C, Tsoulias N, Zude-Sasse M. Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. 'Gala'. FRONTIERS IN PLANT SCIENCE 2021; 12:669909. [PMID: 34326853 PMCID: PMC8315137 DOI: 10.3389/fpls.2021.669909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees.
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Affiliation(s)
- Martin Penzel
- Chair of Agromechatronics, Technische Universität Berlin, Berlin, Germany
- Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
| | - Werner B. Herppich
- Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
| | - Cornelia Weltzien
- Chair of Agromechatronics, Technische Universität Berlin, Berlin, Germany
- Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
| | - Nikos Tsoulias
- Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
| | - Manuela Zude-Sasse
- Horticultural Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
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