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Mertens S, Verbraeken L, Sprenger H, De Meyer S, Demuynck K, Cannoot B, Merchie J, De Block J, Vogel JT, Bruce W, Nelissen H, Maere S, Inzé D, Wuyts N. Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform. PLANT METHODS 2023; 19:132. [PMID: 37996870 PMCID: PMC10668392 DOI: 10.1186/s13007-023-01102-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND Thermography is a popular tool to assess plant water-use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect plant water deficit. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. RESULTS The sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic differences in the plants' water-use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated, including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple TIR indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. CONCLUSION Overall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.
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Affiliation(s)
- Stien Mertens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Lennart Verbraeken
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Heike Sprenger
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Food Safety Department , German Federal Institute for Risk Assessment, Max-Dohrn-Str. 8-10, 10589, Berlin, Germany
| | - Sam De Meyer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Robovision, Technologiepark 80, 9052, Zwijnaarde, Belgium
| | - Kirin Demuynck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Bernard Cannoot
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Julie Merchie
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Eenheid Plant, Instituut voor Landbouw, Visserij-en Voedingsonderzoek (ILVO), Caritasstraat 39, 9090, Melle, Belgium
| | - Jolien De Block
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | | | - Wesley Bruce
- BASF Corporation, 2 TW Alexander Drive, Durham, NC, 27709, USA
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Steven Maere
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
| | - Dirk Inzé
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium.
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium.
| | - Nathalie Wuyts
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, 9052, Zwijnaarde, Belgium
- VIB Center for Plant Systems Biology, Technologiepark 71, 9052, Zwijnaarde, Belgium
- Plant Production Systems, Cultivation Techniques and Varieties in Arable Farming, Agroscope, Route de Duillier 50, 1260, Nyon, Switzerland
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Qin J, Monje O, Nugent MR, Finn JR, O’Rourke AE, Wilson KD, Fritsche RF, Baek I, Chan DE, Kim MS. A hyperspectral plant health monitoring system for space crop production. FRONTIERS IN PLANT SCIENCE 2023; 14:1133505. [PMID: 37469773 PMCID: PMC10352677 DOI: 10.3389/fpls.2023.1133505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/07/2023] [Indexed: 07/21/2023]
Abstract
Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.
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Affiliation(s)
- Jianwei Qin
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Oscar Monje
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Matthew R. Nugent
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Joshua R. Finn
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Aubrie E. O’Rourke
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Kristine D. Wilson
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Ralph F. Fritsche
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Diane E. Chan
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
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3
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Pineda M, Barón M. Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision. PLANTS (BASEL, SWITZERLAND) 2023; 12:1322. [PMID: 36987010 PMCID: PMC10058869 DOI: 10.3390/plants12061322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.
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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 COMMUNICATIONS 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] [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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Hydrogel-extraction technique for non-invasive detection of blue fluorescent substances in plant leaves. Sci Rep 2022; 12:13598. [PMID: 35948743 PMCID: PMC9365774 DOI: 10.1038/s41598-022-17785-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/31/2022] [Indexed: 11/26/2022] Open
Abstract
This paper reports a new hydrogel extraction technique for detecting blue fluorescent substances in plant leaves. These blue fluorescent substances were extracted by placing a hydrogel film on the leaf of a cherry tomato plant infected with Ralstonia solanacearum; herein, chlorogenic acid was confirmed to be a blue fluorescent substance. The wavelength at the maximum fluorescence intensity of the film after the hydrogel extraction was similar to that of the methanolic extract obtained from the infected cherry tomato leaves. Chlorophyll was not extracted from the hydrogel film because no fluorescence peak was observed at 680 nm. Accordingly, the blue fluorescence of the substances extracted from the hydrogel film was not quenched by the strong absorption of chlorophyll in the blue light region. This hydrogel extraction technique can potentially detect small amounts of blue fluorescent substances and the changes in its amount within the leaves of infected plants. These changes in the amount of blue fluorescent substances in the early stages of infection can be used to detect presymptomatic infections. Therefore, hydrogel extraction is a promising technique for the noninvasive detection of infections before onset.
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Pérez-Bueno ML, Illescas-Miranda J, Martín-Forero AF, de Marcos A, Barón M, Fenoll C, Mena M. An extremely low stomatal density mutant overcomes cooling limitations at supra-optimal temperature by adjusting stomatal size and leaf thickness. FRONTIERS IN PLANT SCIENCE 2022; 13:919299. [PMID: 35937324 PMCID: PMC9355609 DOI: 10.3389/fpls.2022.919299] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/27/2022] [Indexed: 05/25/2023]
Abstract
The impact of global warming on transpiration and photosynthesis would compromise plant fitness, impacting on crop yields and ecosystem functioning. In this frame, we explored the performance of a set of Arabidopsis mutants carrying partial or total loss-of-function alleles of stomatal development genes and displaying distinct stomatal abundances. Using microscopy and non-invasive imaging techniques on this genotype collection, we examined anatomical leaf and stomatal traits, plant growth and development, and physiological performance at optimal (22°C) and supra-optimal (30°C) temperatures. All genotypes showed thermomorphogenetic responses but no signs of heat stress. Data analysis singled out an extremely low stomatal abundance mutant, spch-5. At 22°C, spch-5 had lower transpiration and warmer leaves than the wild type. However, at 30°C, this mutant developed larger stomata and thinner leaves, paralleled by a notable cooling capacity, similar to that of the wild type. Despite their low stomatal density (SD), spch-5 plants grown at 30°C showed no photosynthesis or growth penalties. The behavior of spch-5 at supra-optimal temperature exemplifies how the effect of very low stomatal numbers can be counteracted by a combination of larger stomata and thinner leaves. Furthermore, it provides a novel strategy for coping with high growth temperatures.
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Affiliation(s)
- María Luisa Pérez-Bueno
- Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo, Spain
- Departamento de Bioquímica, Biología Celular y Molecular de Plantas, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain
- Departamento de Fisiología Vegetal, Universidad de Granada, Granada, Spain
| | | | - Amanda F. Martín-Forero
- Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo, Spain
| | - Alberto de Marcos
- Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo, Spain
| | - Matilde Barón
- Departamento de Bioquímica, Biología Celular y Molecular de Plantas, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain
| | - Carmen Fenoll
- Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo, Spain
| | - Montaña Mena
- Facultad de Ciencias Ambientales y Bioquímica, Universidad de Castilla-La Mancha, Toledo, Spain
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Pineda M, Pérez-Bueno ML, Barón M. Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris. FRONTIERS IN PLANT SCIENCE 2022; 13:790268. [PMID: 35812917 PMCID: PMC9265216 DOI: 10.3389/fpls.2022.790268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI1-DBI3), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.
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Affiliation(s)
- Mónica Pineda
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
| | - María Luisa Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
- Department of Plant Physiology, Facultad de Farmacia, University of Granada, Granada, Spain
| | - Matilde Barón
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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Grishina A, Sherstneva O, Grinberg M, Zdobnova T, Ageyeva M, Khlopkov A, Sukhov V, Brilkina A, Vodeneev V. Pre-Symptomatic Detection of Viral Infection in Tobacco Leaves Using PAM Fluorometry. PLANTS (BASEL, SWITZERLAND) 2021; 10:2782. [PMID: 34961253 PMCID: PMC8707847 DOI: 10.3390/plants10122782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/11/2021] [Accepted: 12/13/2021] [Indexed: 06/14/2023]
Abstract
Chlorophyll fluorescence imaging was used to study potato virus X (PVX) infection of Nicotiana benthamiana. Infection-induced changes in chlorophyll fluorescence parameters (quantum yield of photosystem II photochemistry (ΦPSII) and non-photochemical fluorescence quenching (NPQ)) in the non-inoculated leaf were recorded and compared with the spatial distribution of the virus detected by the fluorescence of GFP associated with the virus. We determined infection-related changes at different points of the light-induced chlorophyll fluorescence kinetics and at different days after inoculation. A slight change in the light-adapted steady-state values of ΦPSII and NPQ was observed in the infected area of the non-inoculated leaf. In contrast to the steady-state parameters, the dynamics of ΦPSII and NPQ caused by the dark-light transition in healthy and infected areas differed significantly starting from the second day after the detection of the virus in a non-inoculated leaf. The coefficients of correlation between chlorophyll fluorescence parameters and virus localization were 0.67 for ΦPSII and 0.76 for NPQ. In general, the results demonstrate the possibility of reliable pre-symptomatic detection of the spread of a viral infection using chlorophyll fluorescence imaging.
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Affiliation(s)
- Alyona Grishina
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Oksana Sherstneva
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Marina Grinberg
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Tatiana Zdobnova
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Maria Ageyeva
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Andrey Khlopkov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Vladimir Sukhov
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
| | - Anna Brilkina
- Department of Biochemistry and Biotechnology, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (M.A.); (A.B.)
| | - Vladimir Vodeneev
- Department of Biophysics, National Research Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, 603950 Nizhny Novgorod, Russia; (A.G.); (O.S.); (M.G.); (T.Z.); (A.K.); (V.S.)
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10
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Hugelier S, Van den Eynde R, Vandenberg W, Dedecker P. Fluorophore unmixing based on bleaching and recovery kinetics using MCR-ALS. Talanta 2021; 226:122117. [PMID: 33676672 DOI: 10.1016/j.talanta.2021.122117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 02/03/2023]
Abstract
Fluorescence microscopy is a key technology in the life sciences, though its performance is constrained by the number of labels that can be recorded. We propose to use the kinetics of fluorophore photodestruction and subsequent fluorescence recovery to distinguish multiple spectrally-overlapping emitters in fixed cells, thus enhancing the information that can be obtained from a single measurement. We show that the data can be directly processed using multivariate curve resolution - alternating least squares (MCR-ALS) to deliver distinct images for each fluorophore in their local environment, and apply this methodology to membrane imaging using DiBAC4(3) and concanavalin A - Alexa Fluor 488 as the fluorophores. We find that the DiBAC4(3) displays two distinct degradation/recovery kinetics that correspond to two different label distributions, allowing us to simultaneously distinguish three different fluorescence distributions from two spectrally overlapping fluorophores. We expect that our approach will scale to other dynamically-binding dyes, leading to similarly increased multiplexing capability.
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Affiliation(s)
- S Hugelier
- Laboratory for Nanobiology, KU Leuven, B-3001 Leuven, Belgium.
| | - R Van den Eynde
- Laboratory for Nanobiology, KU Leuven, B-3001 Leuven, Belgium
| | - W Vandenberg
- Laboratory for Nanobiology, KU Leuven, B-3001 Leuven, Belgium; Univ. Lille, CNRS, Laboratoire de Spectroscopie pour Les Interactions, La Réactivité et L'Environnement (LASIRE), F-59000 Lille, France
| | - P Dedecker
- Laboratory for Nanobiology, KU Leuven, B-3001 Leuven, Belgium
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11
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Jangra S, Chaudhary V, Yadav RC, Yadav NR. High-Throughput Phenotyping: A Platform to Accelerate Crop Improvement. PHENOMICS (CHAM, SWITZERLAND) 2021; 1:31-53. [PMID: 36939738 PMCID: PMC9590473 DOI: 10.1007/s43657-020-00007-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years. These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments; this is a critical step towards selection of better performing lines as to yield, disease resistance, and stress tolerance to accelerate crop improvement programs. High-throughput phenotyping techniques and platforms help unraveling the genetic basis of complex traits associated with plant growth and development and targeted traits. This review focuses on the advancements in technologies involved in high-throughput, field-based, aerial, and unmanned platforms. Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques, which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.
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Affiliation(s)
- Sumit Jangra
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Vrantika Chaudhary
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Ram C. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
| | - Neelam R. Yadav
- Department of Molecular Biology, Biotechnology, and Bioinformatics, CCS Haryana Agricultural University, Hisar, 125004 India
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12
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Galieni A, D'Ascenzo N, Stagnari F, Pagnani G, Xie Q, Pisante M. Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography. FRONTIERS IN PLANT SCIENCE 2021; 11:609155. [PMID: 33584752 PMCID: PMC7873487 DOI: 10.3389/fpls.2020.609155] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 11/18/2020] [Indexed: 05/24/2023]
Abstract
Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.
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Affiliation(s)
- Angelica Galieni
- Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research and Economics, Monsampolo del Tronto, Italy
| | - Nicola D'Ascenzo
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Fabio Stagnari
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Giancarlo Pagnani
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Qingguo Xie
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Physics and Engineering, Istituto Neurologico Mediterraneo, I.R.C.C.S, Pozzilli, Italy
| | - Michele Pisante
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
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13
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Abstract
In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.
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14
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Li Z, Yu T, Paul R, Fan J, Yang Y, Wei Q. Agricultural nanodiagnostics for plant diseases: recent advances and challenges. NANOSCALE ADVANCES 2020; 2:3083-3094. [PMID: 36134297 PMCID: PMC9417629 DOI: 10.1039/c9na00724e] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 07/06/2020] [Indexed: 05/18/2023]
Abstract
Crop diseases caused by pathogenic microorganisms pose severe threats to the global food supply. Effective diagnostic tools for timely determination of plant diseases become essential to the assurance of agricultural sustainability and global food security. Nucleic acid- and antibody-based molecular assays are gold-standard methodologies for the diagnosis of plant diseases, but the analyzing procedures are complex and laborious. The prominent physical or chemical properties of nanomaterials have enabled their use as innovative and high-performance diagnostic tools for numerous plant pathogens and other important disease biomarkers. Engineered nanomaterials have been incorporated into traditional laboratory molecular assays or sequencing technologies that offer notable enhancement in sensitivity and selectivity. Meanwhile, nanostructure-supported noninvasive detection tools combined with portable imaging devices (e.g., smartphones) have paved the way for fast and on-site diagnosis of plant diseases and long-term monitoring of plant health conditions, especially in resource-poor settings.
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Affiliation(s)
- Zheng Li
- Institute for Advanced Study, Shenzhen University Shenzhen 518060 P. R. China
- Department of Chemical and Biomolecular Engineering, North Carolina State University 911 Partners Way, Campus Box 7905 Raleigh NC 27695 USA
| | - Tao Yu
- Department of Chemical and Biomolecular Engineering, North Carolina State University 911 Partners Way, Campus Box 7905 Raleigh NC 27695 USA
| | - Rajesh Paul
- Department of Chemical and Biomolecular Engineering, North Carolina State University 911 Partners Way, Campus Box 7905 Raleigh NC 27695 USA
| | - Jingyuan Fan
- Department of Polymer Science and Engineering, Zhejiang University Hangzhou 310027 P. R. China
| | - Yuming Yang
- Department of Agrotechnology and Food Sciences, Wageningen University 6708 PB Wageningen The Netherlands
| | - Qingshan Wei
- Department of Chemical and Biomolecular Engineering, North Carolina State University 911 Partners Way, Campus Box 7905 Raleigh NC 27695 USA
- Emerging Plant Disease and Global Food Security Cluster, North Carolina State University USA
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15
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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16
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Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR. SENSORS 2020; 20:s20030935. [PMID: 32050619 PMCID: PMC7038968 DOI: 10.3390/s20030935] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/07/2020] [Accepted: 02/09/2020] [Indexed: 11/16/2022]
Abstract
Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.
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Paul A, Ghosh S, Das AK, Goswami S, Das Choudhury S, Sen S. A Review on Agricultural Advancement Based on Computer Vision and Machine Learning. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2020. [DOI: 10.1007/978-981-13-7403-6_50] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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18
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Beć KB, Grabska J, Bonn GK, Popp M, Huck CW. Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review. FRONTIERS IN PLANT SCIENCE 2020; 11:1226. [PMID: 32849759 PMCID: PMC7427587 DOI: 10.3389/fpls.2020.01226] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/27/2020] [Indexed: 05/08/2023]
Abstract
Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.
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Affiliation(s)
- Krzysztof B. Beć
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
| | - Justyna Grabska
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
| | - Günther K. Bonn
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- ADSI, Austrian Drug Screening Institute, Innsbruck, Austria
| | - Michael Popp
- Michael Popp Research Institute for New Phyto Entities, University of Innsbruck, Innsbruck, Austria
| | - Christian W. Huck
- CCB-Center for Chemistry and Biomedicine, Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innsbruck, Austria
- *Correspondence: Krzysztof B. Beć, ; Christian W. Huck,
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19
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Tang L, Qiu L, Liu C, Du G, Mo Z, Tang X, Mao Y. Transcriptomic Insights into Innate Immunity Responding to Red Rot Disease in Red Alga Pyropia yezoensis. Int J Mol Sci 2019; 20:E5970. [PMID: 31783543 PMCID: PMC6928737 DOI: 10.3390/ijms20235970] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/22/2019] [Accepted: 11/24/2019] [Indexed: 01/17/2023] Open
Abstract
Pyropia yezoensis, one of the most economically important marine algae, suffers from the biotic stress of the oomycete necrotrophic pathogen Pythium porphyrae. However, little is known about the molecular defensive mechanisms employed by Pyr. yezoensis during the infection process. In the present study, we defined three stages of red rot disease based on histopathological features and photosynthetic physiology. Transcriptomic analysis was carried out at different stages of infection to identify the genes related to the innate immune system in Pyr. yezoensis. In total, 2139 up-regulated genes and 1672 down-regulated genes were identified from all the infected groups. Pathogen receptor genes, including three lectin genes (pattern recognition receptors (PRRs)) and five genes encoding typical plant R protein domains (leucine rich repeat (LRR), nucleotide binding site (NBS), or Toll/interleukin-1 receptor (TIR)), were found to be up-regulated after infection. Several defense mechanisms that were typically regarded as PAMP-triggered immunity (PTI) in plants were induced during the infection. These included defensive and protective enzymes, heat shock proteins, secondary metabolites, cellulase, and protease inhibitors. As a part of the effector-triggered immunity (ETI), the expression of genes related to the ubiquitin-proteasome system (UPS) and hypersensitive cell death response (HR) increased significantly during the infection. The current study suggests that, similar to plants, Pyr. yezoensis possesses a conserved innate immune system that counters the invasion of necrotrophic pathogen Pyt. porphyrae. However, the innate immunity genes of Pyr. yezoensis appear to be more ancient in origin compared to those in higher plants.
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Affiliation(s)
- Lei Tang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
| | - Liping Qiu
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
| | - Cong Liu
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
| | - Zhaolan Mo
- Key Laboratory of Maricultural Organism Disease Control, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
| | - Xianghai Tang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
| | - Yunxiang Mao
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China; (L.T.); (L.Q.); (C.L.); (G.D.); (X.T.)
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya 572022, China
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20
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Hupp S, Rosenkranz M, Bonfig K, Pandey C, Roitsch T. Noninvasive Phenotyping of Plant-Pathogen Interaction: Consecutive In Situ Imaging of Fluorescing Pseudomonas syringae, Plant Phenolic Fluorescence, and Chlorophyll Fluorescence in Arabidopsis Leaves. FRONTIERS IN PLANT SCIENCE 2019; 10:1239. [PMID: 31681362 PMCID: PMC6803544 DOI: 10.3389/fpls.2019.01239] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Accepted: 09/05/2019] [Indexed: 05/26/2023]
Abstract
Plant-pathogen interactions have been widely studied, but mostly from the site of the plant secondary defense. Less is known about the effects of pathogen infection on plant primary metabolism. The possibility to transform a fluorescing protein into prokaryotes is a promising phenotyping tool to follow a bacterial infection in plants in a noninvasive manner. In the present study, virulent and avirulent Pseudomonas syringae strains were transformed with green fluorescent protein (GFP) to follow the spread of bacteria in vivo by imaging Pulse-Amplitude-Modulation (PAM) fluorescence and conventional binocular microscopy. The combination of various wavelengths and filters allowed simultaneous detection of GFP-transformed bacteria, PAM chlorophyll fluorescence, and phenolic fluorescence from pathogen-infected plant leaves. The results show that fluorescence imaging allows spatiotemporal monitoring of pathogen spread as well as phenolic and chlorophyll fluorescence in situ, thus providing a novel means to study complex plant-pathogen interactions and relate the responses of primary and secondary metabolism to pathogen spread and multiplication. The study establishes a deeper understanding of imaging data and their implementation into disease screening.
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Affiliation(s)
- Sabrina Hupp
- Department of Pharmaceutical Biology, University of Würzburg, Würzburg, Germany
| | - Maaria Rosenkranz
- Department of Pharmaceutical Biology, University of Würzburg, Würzburg, Germany
- Department of Environmental Sciences, Institute of Biochemical Plant Pathology, Research Unit Environmental Simulation, Helmholtz Zentrum Muenchen, Neuherberg, Germany
| | - Katharina Bonfig
- Department of Pharmaceutical Biology, University of Würzburg, Würzburg, Germany
| | - Chandana Pandey
- Department of Plant and Environmental Sciences, Section of Crop Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Roitsch
- Department of Pharmaceutical Biology, University of Würzburg, Würzburg, Germany
- Department of Plant and Environmental Sciences, Section of Crop Science, University of Copenhagen, Copenhagen, Denmark
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czechia
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Costa JM, Marques da Silva J, Pinheiro C, Barón M, Mylona P, Centritto M, Haworth M, Loreto F, Uzilday B, Turkan I, Oliveira MM. Opportunities and Limitations of Crop Phenotyping in Southern European Countries. FRONTIERS IN PLANT SCIENCE 2019; 10:1125. [PMID: 31608085 PMCID: PMC6774291 DOI: 10.3389/fpls.2019.01125] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 08/15/2019] [Indexed: 05/31/2023]
Abstract
The Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of "phytotron research" to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainability.
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Affiliation(s)
| | - Jorge Marques da Silva
- Biosystems and Integrative Sciences Institute (BioISI), Faculty of Sciences, Universidade de Lisboa, Lisbon, Portugal
| | - Carla Pinheiro
- FCT NOVA, Universidade Nova de Lisboa, Monte da Caparica, Portugal
- ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Matilde Barón
- Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain
| | - Photini Mylona
- HAO-DEMETER, Institute of Plant Breeding and Genetic Resources, Thermi, Greece
| | - Mauro Centritto
- Institute for Sustainable Plant Protection, Italian National Research Council (IPSP-CNR), Sesto Fiorentino, Italy
| | | | - Francesco Loreto
- Department of Biology, Agriculture and Food Sciences, CNR, Rome, Italy
| | - Baris Uzilday
- Department of Biology, Faculty of Science, Ege University, I˙zmir, Turkey
| | - Ismail Turkan
- Department of Biology, Faculty of Science, Ege University, I˙zmir, Turkey
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22
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Segečová A, Pérez-Bueno ML, Barón M, Červený J, Roitsch TG. Noninvasive determination of toxic stress biomarkers by high-throughput screening of photoautotrophic cell suspension cultures with multicolor fluorescence imaging. PLANT METHODS 2019; 15:100. [PMID: 31462906 PMCID: PMC6708129 DOI: 10.1186/s13007-019-0484-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 08/14/2019] [Indexed: 05/06/2023]
Abstract
BACKGROUND With increasing pollution, herbicide application and interest in plant phenotyping, sensors capturing early responses to toxic stress are demanded for screening susceptible or resistant plant varieties. Standard toxicity tests on plants are laborious, demanding in terms of space and material, and the measurement of growth-inhibition based endpoints takes relatively long time. The aim of this work was to explore the potential of photoautotrophic cell suspension cultures for high-throughput early toxicity screening based on imaging techniques. The investigation of the universal potential of fluorescence imaging methods involved testing of three toxicants with different modes of action (DCMU, glyphosate and chromium). RESULTS The increased pace of testing was achieved by using non-destructive imaging methods-multicolor fluorescence (MCF) and chlorophyll fluorescence (ChlF). These methods detected the negative effects of the toxicants earlier than it was reflected in plant growth inhibition (decrease in leaf area and final dry weight). Moreover, more subtle and transient effects not resulting in growth inhibition could be detected by fluorescence. The pace and sensitivity of stress detection was further enhanced by using photoautotrophic cell suspension cultures. These reacted sooner, more pronouncedly and to lower concentrations of the tested toxicants than the plants. Toxicant-specific stress signatures were observed as a combination of MCF and ChlF parameters and timing of the response. Principal component analysis was found to be useful for reduction of the collected multidimensional data sets to a few informative parameters allowing comparison of the toxicant signatures. CONCLUSIONS Photoautotrophic cell suspension cultures have proved to be useful for rapid high-throughput screening of toxic stress and display a potential for employment as an alternative to tests on whole plants. The MCF and ChlF methods are capable of distinguishing early stress signatures of at least three different modes of action.
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Affiliation(s)
- Anna Segečová
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, 603 00 Brno, Czech Republic
- RECETOX, Masaryk University, 625 00 Brno, Czech Republic
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark
| | - María Luisa Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, CSIC, 18008 Granada, Spain
| | - Matilde Barón
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, CSIC, 18008 Granada, Spain
| | - Jan Červený
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, 603 00 Brno, Czech Republic
| | - Thomas Georg Roitsch
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, 603 00 Brno, Czech Republic
- Department of Plant and Environmental Sciences, University of Copenhagen, 1871 Frederiksberg, Denmark
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Pérez-Bueno ML, Pineda M, Vida C, Fernández-Ortuño D, Torés JA, de Vicente A, Cazorla FM, Barón M. Detection of White Root Rot in Avocado Trees by Remote Sensing. PLANT DISEASE 2019; 103:1119-1125. [PMID: 30995422 DOI: 10.1094/pdis-10-18-1778-re] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
White root rot, caused by the soilborne fungus Rosellinia necatrix, is an important constraint to production for a wide range of woody crop plants such as avocado trees. The current methods of detection of white root rot are based on microbial and molecular techniques, and their application at orchard scale is limited. In this study, physiological parameters provided by imaging techniques were analyzed by machine learning methods. Normalized difference vegetation index (NDVI) and normalized canopy temperature (canopy temperature - air temperature) were tested as predictors of disease by several algorithms. Among them, logistic regression analysis (LRA) trained on NDVI data showed the highest sensitivity and lowest rate of false negatives. This algorithm based on NDVI could be a quick and feasible method to detect trees potentially affected by white root rot in avocado orchards.
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Affiliation(s)
- M L Pérez-Bueno
- 1 Department of Biochemistry and Molecular and Cellular Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Profesor Albareda, 1, 18008, Granada, Spain
| | - M Pineda
- 1 Department of Biochemistry and Molecular and Cellular Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Profesor Albareda, 1, 18008, Granada, Spain
| | - C Vida
- 2 Department of Microbiology, Science Faculty of Málaga University, Institute for Mediterranean and Subtropical Horticulture "La Mayora", Málaga University, Spanish National Research Council (IHSM-UMA-CSIC), Boulevard Louis Pasteur, 31, 29071, Málaga, Spain; and
| | - D Fernández-Ortuño
- 2 Department of Microbiology, Science Faculty of Málaga University, Institute for Mediterranean and Subtropical Horticulture "La Mayora", Málaga University, Spanish National Research Council (IHSM-UMA-CSIC), Boulevard Louis Pasteur, 31, 29071, Málaga, Spain; and
| | - J A Torés
- 3 Department of Plant Protection, IHSM-UMA-CSIC, Avenue Dr. Wienberg, 29750, Algarrobo-Costa (Málaga), Spain
| | - A de Vicente
- 2 Department of Microbiology, Science Faculty of Málaga University, Institute for Mediterranean and Subtropical Horticulture "La Mayora", Málaga University, Spanish National Research Council (IHSM-UMA-CSIC), Boulevard Louis Pasteur, 31, 29071, Málaga, Spain; and
| | - F M Cazorla
- 2 Department of Microbiology, Science Faculty of Málaga University, Institute for Mediterranean and Subtropical Horticulture "La Mayora", Málaga University, Spanish National Research Council (IHSM-UMA-CSIC), Boulevard Louis Pasteur, 31, 29071, Málaga, Spain; and
| | - M Barón
- 1 Department of Biochemistry and Molecular and Cellular Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Profesor Albareda, 1, 18008, Granada, Spain
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Improvement of growth and yield of soybean plants through the application of non-thermal plasmas to seeds with different health status. Heliyon 2019; 5:e01495. [PMID: 31011650 PMCID: PMC6462543 DOI: 10.1016/j.heliyon.2019.e01495] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/20/2019] [Accepted: 04/05/2019] [Indexed: 12/31/2022] Open
Abstract
Soybean (Glycine max (L.) Merrill) is a globally important crop, providing oil and protein. Diaporthe/Phomopsis complex includes seed-borne pathogens that affect this legume. Non-thermal plasma treatment is a fast, cost-effective and environmental-friendly technology. Soybean seeds were exposed to a quasi-stationary (50 Hz) dielectric barrier discharge plasma operating at atmospheric pressure air. Different carrying gases (O2 and N2) and barrier insulating materials were used. This work was performed to test if the effects of non-thermal plasma treatment applied to healthy and infected seeds persist throughout the entire cycle of plants. To this aim, lipid peroxidation, activity of catalase, superoxide dismutase and guaiacol peroxidase, vegetative growth and agronomic traits were analysed. The results here reported showed that plants grown from infected seeds did not trigger oxidative stress due to the reduction of pathogen incidence in seeds treated with cold plasma. Vegetative growth revealed a similar pattern for plants grown from treated seeds than that found for the healthy control. Infected control, by contrast, showed clear signs of damage. Moreover, plasma treatment itself increased plant growth, promoted a normal and healthy physiological performance and incremented the yield of plants. The implementation of this technology for seeds treatment before sowing could help reducing the use of agrochemicals during the crop cycle.
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Yao J, Sun D, Cen H, Xu H, Weng H, Yuan F, He Y. Phenotyping of Arabidopsis Drought Stress Response Using Kinetic Chlorophyll Fluorescence and Multicolor Fluorescence Imaging. FRONTIERS IN PLANT SCIENCE 2018; 9:603. [PMID: 29868063 PMCID: PMC5958224 DOI: 10.3389/fpls.2018.00603] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/16/2018] [Indexed: 05/21/2023]
Abstract
Plant responses to drought stress are complex due to various mechanisms of drought avoidance and tolerance to maintain growth. Traditional plant phenotyping methods are labor-intensive, time-consuming, and subjective. Plant phenotyping by integrating kinetic chlorophyll fluorescence with multicolor fluorescence imaging can acquire plant morphological, physiological, and pathological traits related to photosynthesis as well as its secondary metabolites, which will provide a new means to promote the progress of breeding for drought tolerant accessions and gain economic benefit for global agriculture production. Combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging proved to be efficient for the early detection of drought stress responses in the Arabidopsis ecotype Col-0 and one of its most affected mutants called reduced hyperosmolality-induced [Ca2+]i increase 1. Kinetic chlorophyll fluorescence curves were useful for understanding the drought tolerance mechanism of Arabidopsis. Conventional fluorescence parameters provided qualitative information related to drought stress responses in different genotypes, and the corresponding images showed spatial heterogeneities of drought stress responses within the leaf and the canopy levels. Fluorescence parameters selected by sequential forward selection presented high correlations with physiological traits but not morphological traits. The optimal fluorescence traits combined with the support vector machine resulted in good classification accuracies of 93.3 and 99.1% for classifying the control plants from the drought-stressed ones with 3 and 7 days treatments, respectively. The results demonstrated that the combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging with the machine learning technique was capable of providing comprehensive information of drought stress effects on the photosynthesis and the secondary metabolisms. It is a promising phenotyping technique that allows early detection of plant drought stress.
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Affiliation(s)
- Jieni Yao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
| | - Dawei Sun
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
| | - Haixia Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
| | - Haiyong Weng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
| | - Fang Yuan
- Center for Plant Environmental Sensing, College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China
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26
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Pineda M, Pérez-Bueno ML, Barón M. Detection of Bacterial Infection in Melon Plants by Classification Methods Based on Imaging Data. FRONTIERS IN PLANT SCIENCE 2018; 9:164. [PMID: 29491881 PMCID: PMC5817087 DOI: 10.3389/fpls.2018.00164] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 01/29/2018] [Indexed: 05/22/2023]
Abstract
The bacterium Dickeya dadantii is responsible of important economic losses in crop yield worldwide. In melon leaves, D. dadantii produced multiple necrotic spots surrounded by a chlorotic halo, followed by necrosis of the whole infiltrated area and chlorosis in the surrounding tissues. The extent of these symptoms, as well as the day of appearance, was dose-dependent. Several imaging techniques (variable chlorophyll fluorescence, multicolor fluorescence, and thermography) provided spatial and temporal information about alterations in the primary and secondary metabolism, as well as the stomatal activity in the infected leaves. Detection of diseased leaves was carried out by using machine learning on the numerical data provided by these imaging techniques. Mathematical algorithms based on data from infiltrated areas offered 96.5 to 99.1% accuracy when classifying them as mock vs. bacteria-infiltrated. These algorithms also showed a high performance of classification of whole leaves, providing accuracy values of up to 96%. Thus, the detection of disease on whole leaves by a model trained on infiltrated areas appears as a reliable method that could be scaled-up for use in plant breeding programs or precision agriculture.
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Affiliation(s)
| | - María L. Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council, Granada, Spain
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Gutiérrez S, Fernández-Novales J, Diago MP, Tardaguila J. On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties. FRONTIERS IN PLANT SCIENCE 2018; 9:1102. [PMID: 30090110 PMCID: PMC6068396 DOI: 10.3389/fpls.2018.01102] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 07/09/2018] [Indexed: 05/22/2023]
Abstract
Grapevine varietal classification is an important plant phenotyping issue for grape growing and wine industry. This task has been achieved from destructive techniques like classic ampelography and DNA analysis under laboratory conditions. This work displays a new approach for the classification of a high number of grapevine (Vitis vinifera L.) varieties under field conditions using on-the-go hyperspectral imaging and different machine learning algorithms. On-the-go imaging was performed under natural illumination using a hyperspectral camera mounted on an all-terrain vehicle at 5 km/h. Spectra were acquired over two different leaf phenological stages on the canopy of 30 different varieties on a commercial vineyard located in La Rioja, Spain. A total of 1,200 spectral samples were generated. Support vector machines (SVM) and artificial neural networks (multilayer perceptrons, MLP) were used for the development of a large number of models, testing different algorithm parameters and spectral pre-processing techniques. Both classifiers yielded notable performance values and were able to train models with recall F1 scores and area under the receiver operating characteristic curve marks up to 0.99 for 5-fold cross validation. Statistical analyses supported that the best SVM kernel was linear and the best activation function for MLP was the hyperbolic tangent function. The prediction performance for individual varieties of MLP ranged from 0.94 to 0.99, displaying low levels of variability. In the case of SVM, slightly higher differences were obtained, ranging from 0.83 to 0.97 for individual varieties. These results support the possibility of deploying an on-the-go hyperspectral imaging system in the field capable of successfully classifying leaves from different grapevine varieties. This technology could thus be considered as a new useful non-destructive tool for plant phenotyping under field conditions.
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Perez-Sanz F, Navarro PJ, Egea-Cortines M. Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms. Gigascience 2017; 6:1-18. [PMID: 29048559 PMCID: PMC5737281 DOI: 10.1093/gigascience/gix092] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 06/20/2017] [Accepted: 09/17/2017] [Indexed: 11/25/2022] Open
Abstract
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.
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Affiliation(s)
- Fernando Perez-Sanz
- Genetics, ETSIA, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Pedro J Navarro
- Genetics, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain
| | - Marcos Egea-Cortines
- Genetics, ETSIA, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
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