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Cubero J, Zarco-Tejada PJ, Cuesta-Morrondo S, Palacio-Bielsa A, Navas-Cortés JA, Sabuquillo P, Poblete T, Landa BB, Garita-Cambronero J. New Approaches to Plant Pathogen Detection and Disease Diagnosis. PHYTOPATHOLOGY 2024; 114:1989-2006. [PMID: 39264350 DOI: 10.1094/phyto-10-23-0366-ia] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.
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Affiliation(s)
- Jaime Cubero
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Pablo J Zarco-Tejada
- School of Agriculture, Food and Ecosystem Sciences, Faculty of Science and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, VIC, Australia
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | - Sara Cuesta-Morrondo
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ana Palacio-Bielsa
- Centro de Investigación y Tecnología Agroalimentaria de Aragón-Instituto Agroalimentario de Aragón-IA2 (CITA-Universidad de Zaragoza), Zaragoza, Spain
| | - Juan A Navas-Cortés
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | - Pilar Sabuquillo
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain
| | - Tomás Poblete
- School of Agriculture, Food and Ecosystem Sciences, Faculty of Science and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, VIC, Australia
| | - Blanca B Landa
- Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
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Shymanovich T, Saville AC, Paul R, Wei Q, Ristaino JB. Rapid Detection of Viral, Bacterial, Fungal, and Oomycete Pathogens on Tomatoes with Microneedles, LAMP on a Microfluidic Chip, and Smartphone Device. PHYTOPATHOLOGY 2024; 114:1975-1983. [PMID: 38829831 DOI: 10.1094/phyto-12-23-0481-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Rapid detection of plant diseases before they escalate can improve disease control. Our team has developed rapid nucleic acid extraction methods with microneedles and combined these with loop-mediated amplification (LAMP) assays for pathogen detection in the field. In this work, we developed LAMP assays for early blight (Alternaria linariae, A. alternata, and A. solani) and bacterial spot of tomato (Xanthomonas perforans) and validated these LAMP assays and two previously developed LAMP assays for tomato spotted wilt virus and late blight. Tomato plants were inoculated, and disease severity was measured. Extractions were performed using microneedles, and LAMP assays were run in tubes (with hydroxynaphthol blue) on a heat block or on a newly designed microfluidic slide chip on a heat block or a slide heater. Fluorescence on the microfluidic chip slides was visualized using EvaGreen and photographed on a smartphone. Plants inoculated with X. perforans or tomato spotted wilt virus tested positive prior to visible disease symptoms, whereas Phytophthora infestans and A. linariae were detected at the time of visual disease symptoms. LAMP assays were more sensitive than PCR, and the limit of detection was 1 pg of DNA for both A. linariae and X. perforans. The LAMP assay designed for early blight detected all three species of Alternaria that infect tomato and is thus an Alternaria spp. assay. This study demonstrates the utility of rapid microneedle extraction followed by LAMP on a microfluidic chip for rapid diagnosis of four important tomato pathogens.
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Affiliation(s)
- Tatsiana Shymanovich
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC
| | - Amanda C Saville
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC
| | - Rajesh Paul
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC
| | - Qingshan Wei
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC
- Emerging Plant Disease and Global Food Security Cluster, Plant Sciences Initiative, North Carolina State University, Raleigh, NC
| | - Jean Beagle Ristaino
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC
- Emerging Plant Disease and Global Food Security Cluster, Plant Sciences Initiative, North Carolina State University, Raleigh, NC
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Wu KT, Spychalla P, Pereyra M, Liou M, Chen Y, Silva E, Gevens A. Impacts of a Commercially Available Horticultural Oil Biopesticide (EF-400) on the Tomato- Phytophthora infestans Pathosystem. PLANT DISEASE 2024; 108:1533-1543. [PMID: 38105459 DOI: 10.1094/pdis-12-22-2968-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Biopesticide fungicides are naturally derived compounds that offer protection from plant diseases through various modes of action, including antimicrobial activity and upregulation of defense responses in host plants. These plant protectants provide a sustainable and safe alternative to conventional pesticides in integrated disease management programs and are especially salient in the management of high-risk and economically important diseases such as late blight of tomato and potato, for which host resistance options are limited. In this study, a commercially available biopesticide, EF400 comprised of clove (8.2%), rosemary (8.1%), and peppermint oils (6.7%) (Anjon AG, Corcoran, CA), was investigated for its effects on the Phytophthora infestans-tomato pathosystem. Specifically, we evaluated the impact of EF400 on P. infestans growth in culture, disease symptoms in planta, and activation of host defenses through monitoring transcript accumulation of defense-related genes. The application timing and use rate of EF400 were further investigated for managing tomato late blight. EF400 delayed the onset of tomato late blight symptoms, although not as effectively as the copper hydroxide fungicide Champ WG (Nufarm Americas Inc., Alsip, IL). Pathogen mycelial growth and sporangial quantity on late blight-susceptible tomato leaves were significantly reduced with EF400. The biopesticide also had an enhancing or suppressing effect on the transcript accumulation of three defense-related genes: Pin2, PR1a, and PR1b. Our work in exploring a commercially available horticultural oil biopesticide meaningfully contributed to the essential knowledge base for optimizing recommendations for the management of tomato late blight.
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Affiliation(s)
- Kuantin Tina Wu
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
| | - Pia Spychalla
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
| | - Matthew Pereyra
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
| | - Michael Liou
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706
| | - Yu Chen
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
| | - Erin Silva
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
| | - Amanda Gevens
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706
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Gambhir N, Paul A, Qiu T, Combs DB, Hosseinzadeh S, Underhill A, Jiang Y, Cadle-Davidson LE, Gold KM. Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine. PHYTOPATHOLOGY 2024; 114:464-473. [PMID: 37565813 DOI: 10.1094/phyto-02-23-0061-r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Frequent fungicide applications are required to manage grapevine powdery mildew (Erysiphe necator). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence. The goal of this study was to evaluate if hyperspectral sensing in the visible to shortwave infrared range (400 to 2,400 nm) can quantify foliar fungicide efficacy on grape leaves. Commercial formulations of metrafenone, Bacillus mycoides isolate J (BmJ), and sulfur were applied on Chardonnay grapevines in vineyard or greenhouse settings. Foliar reflectance was measured with handheld hyperspectral spectroradiometers at multiple days post-application. Fungicide efficacy was estimated as a proxy for fungicide residue and disease control measured with the Blackbird microscopy imaging robot. Treatments could be differentiated from the untreated control with an accuracy of 73.06% for metrafenone, 67.76% for BmJ, and 94.10% for sulfur. The change in spectral reflectance was moderately correlated with the cube root of the area under the disease progress curve for metrafenone- and sulfur-treated samples (R2 = 0.38 and 0.36, respectively) and with sulfur residue (R2 = 0.42). BmJ treatment impacted foliar physiology by enhancing the leaf mass/area and reducing the nitrogen and total phenolic content as estimated from spectral reflectance. The results suggest that hyperspectral sensing can be used to monitor in-situ fungicide efficacy, and the prediction accuracy depends on the fungicide and the time point measured. The ability to monitor in-situ fungicide efficacy could facilitate more strategic fungicide applications and promote sustainable grapevine protection. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
- Nikita Gambhir
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | - Angela Paul
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | - Tian Qiu
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | - David B Combs
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | - Saeed Hosseinzadeh
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | - Anna Underhill
- U.S. Department of Agriculture Grape Genetics Research Unit, Geneva, NY 14456
| | - Yu Jiang
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
| | | | - Kaitlin M Gold
- School of Integrative Plant Sciences, College of Agriculture and Life Sciences, Cornell AgriTech, Cornell University, Geneva, NY 14456
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Neri I, Caponi S, Bonacci F, Clementi G, Cottone F, Gammaitoni L, Figorilli S, Ortenzi L, Aisa S, Pallottino F, Mattarelli M. Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture. SENSORS (BASEL, SWITZERLAND) 2024; 24:344. [PMID: 38257437 PMCID: PMC10820832 DOI: 10.3390/s24020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
Abstract
In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps.
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Affiliation(s)
- Igor Neri
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
| | - Silvia Caponi
- Materials Foundry (IOM-CNR), National Research Council, c/o Department of Physics and Geology, Via A. Pascoli, 06123 Perugia, Italy
| | - Francesco Bonacci
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
| | - Giacomo Clementi
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
| | - Francesco Cottone
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
| | - Luca Gammaitoni
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
| | - Simone Figorilli
- Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
| | - Luciano Ortenzi
- Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
- Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, Via S. Camillo De Lellis, Via Angelo Maria Ricci, 35a-02100 Rieti, 01100 Viterbo, Italy
| | - Simone Aisa
- Materials Foundry (IOM-CNR), National Research Council, c/o Department of Physics and Geology, Via A. Pascoli, 06123 Perugia, Italy
| | - Federico Pallottino
- Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy
| | - Maurizio Mattarelli
- Department of Physics and Geology, University of Perugia, Via A. Pascoli, 06123 Perugia, Italy
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Zhang B, Ou Y, Yu S, Liu Y, Liu Y, Qiu W. Gray mold and anthracnose disease detection on strawberry leaves using hyperspectral imaging. PLANT METHODS 2023; 19:148. [PMID: 38115023 PMCID: PMC10729489 DOI: 10.1186/s13007-023-01123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/04/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND Gray mold and anthracnose are the main factors affecting strawberry quality and yield. Accurate and rapid early disease identification is of great significance to achieve precise targeted spraying to avoid large-scale spread of diseases and improve strawberry yield and quality. However, the characteristics between early disease infected and healthy leaves are very similar, making the early identification of strawberry gray mold and anthracnose still a challenge. RESULTS Based on hyperspectral imaging technology, this study explored the potential of combining spectral fingerprint features and vegetation indices (VIs) for early detection (24-h infected) of strawberry leaves diseases. The competitive adaptive reweighted sampling (CARS) algorithm and ReliefF algorithm were used for the extraction of spectral fingerprint features and VIs, respectively. Three machine learning models, Backpropagation Neural Network (BPNN), Support Vector Machine (SVM) and Random Forest (RF), were developed for the early identification of strawberry gray mold and anthracnose, using spectral fingerprint, VIs and their combined features as inputs respectively. The results showed that the combination of spectral fingerprint features and VIs had better recognition accuracy compared with individual features as inputs, and the accuracies of the three classifiers (BPNN, SVM and RF) were 97.78%, 94.44%, and 93.33%, respectively, which indicate that the fusion features approach proposed in this study can effectively improve the early detection performance of strawberry leaves diseases. CONCLUSIONS This study provided an accurate, rapid, and nondestructive recognition of strawberry gray mold and anthracnose disease in early stage.
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Affiliation(s)
- Baohua Zhang
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yunmeng Ou
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Shuwan Yu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Yuchen Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Ying Liu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu, P.R. China
| | - Wei Qiu
- College of Engineering, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Taishan Street, Pukou District, Nanjing, Jiangsu, 210031, P.R. China.
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [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: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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8
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Attaluri S, Dharavath R. Novel plant disease detection techniques-a brief review. Mol Biol Rep 2023; 50:9677-9690. [PMID: 37823933 DOI: 10.1007/s11033-023-08838-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
Plant pathogens cause severe losses to agricultural yield worldwide. Tracking plant health and early disease detection is important to reduce the disease spread and thus economic loss. Though visual scouting has been practiced from former times, detection of asymptomatic disease conditions is difficult. So, DNA-based and serological methods gained importance in plant disease detection. The progress in advanced technologies challenges the development of rapid, non-invasive, and on-field detection techniques such as spectroscopy. This review highlights various direct and indirect ways of detecting plant diseases like Enzyme-linked immunosorbent assay, Lateral flow assays, Polymerase chain reaction, spectroscopic techniques and biosensors. Although these techniques are sensitive and pathogen-specific, they are more laborious and time-intensive. As a consequence, a lot of interest is gained in in-field adaptable point-of-care devices with artificial intelligence-assisted pathogen detection at an early stage. More recently computer-aided techniques like neural networks are gaining significance in plant disease detection by image processing. In addition, a concise report on the latest progress achieved in plant disease detection techniques is provided.
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Agho CA, Kaurilind E, Tähtjärv T, Runno-Paurson E, Niinemets Ü. Comparative transcriptome profiling of potato cultivars infected by late blight pathogen Phytophthora infestans: Diversity of quantitative and qualitative responses. Genomics 2023; 115:110678. [PMID: 37406973 PMCID: PMC10548088 DOI: 10.1016/j.ygeno.2023.110678] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 06/30/2023] [Accepted: 07/02/2023] [Indexed: 07/07/2023]
Abstract
The Estonia potato cultivar Ando has shown elevated field resistance to Phytophthora infestans, even after being widely grown for over 40 years. A comprehensive transcriptional analysis was performed using RNA-seq from plant leaf tissues to gain insight into the mechanisms activated for the defense after infection. Pathogen infection in Ando resulted in about 5927 differentially expressed genes (DEGs) compared to 1161 DEGs in the susceptible cultivar Arielle. The expression levels of genes related to plant disease resistance such as serine/threonine kinase activity, signal transduction, plant-pathogen interaction, endocytosis, autophagy, mitogen-activated protein kinase (MAPK), and others were significantly enriched in the upregulated DEGs in Ando, whereas in the susceptible cultivar, only the pathway related to phenylpropanoid biosynthesis was enriched in the upregulated DEGs. However, in response to infection, photosynthesis was deregulated in Ando. Multi-signaling pathways of the salicylic-jasmonic-ethylene biosynthesis pathway were also activated in response to Phytophthora infestans infection.
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Affiliation(s)
- C A Agho
- Chair of Crop Science and Plant Biology, Estonian University of Life Sciences, Kreutzwaldi 1, Tartu 51006, Estonia.
| | - E Kaurilind
- Chair of Crop Science and Plant Biology, Estonian University of Life Sciences, Kreutzwaldi 1, Tartu 51006, Estonia
| | - T Tähtjärv
- Centre of Estonian Rural Research and Knowledge, J. Aamisepa 1, 48309 Jõgeva, Estonia
| | - E Runno-Paurson
- Chair of Crop Science and Plant Biology, Estonian University of Life Sciences, Kreutzwaldi 1, Tartu 51006, Estonia
| | - Ü Niinemets
- Chair of Crop Science and Plant Biology, Estonian University of Life Sciences, Kreutzwaldi 1, Tartu 51006, Estonia; Estonian Academy of Sciences, Kohtu 6, Tallinn 10130, Estonia
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Reis Pereira M, dos Santos FN, Tavares F, Cunha M. Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling. FRONTIERS IN PLANT SCIENCE 2023; 14:1242201. [PMID: 37662158 PMCID: PMC10468592 DOI: 10.3389/fpls.2023.1242201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.
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Affiliation(s)
- Mafalda Reis Pereira
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Filipe Neves dos Santos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Fernando Tavares
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vairão, Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vairão, Portugal
| | - Mário Cunha
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
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11
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Galvan FER, Pavlick R, Trolley G, Aggarwal S, Sousa D, Starr C, Forrestel E, Bolton S, Alsina MDM, Dokoozlian N, Gold KM. Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy. PHYTOPATHOLOGY 2023; 113:1439-1446. [PMID: 37097472 DOI: 10.1094/phyto-01-23-0030-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The U.S. wine and grape industry loses $3B annually due to viral diseases including grapevine leafroll-associated virus complex 3 (GLRaV-3). Current detection methods are labor-intensive and expensive. GLRaV-3 has a latent period in which the vines are infected but do not display visible symptoms, making it an ideal model to evaluate the scalability of imaging spectroscopy-based disease detection. The NASA Airborne Visible and Infrared Imaging Spectrometer Next Generation was deployed to detect GLRaV-3 in Cabernet Sauvignon grapevines in Lodi, CA in September 2020. Foliage was removed from the vines as part of mechanical harvest soon after image acquisition. In September of both 2020 and 2021, industry collaborators scouted 317 hectares on a vine-by-vine basis for visible viral symptoms and collected a subset for molecular confirmation testing. Symptomatic grapevines identified in 2021 were assumed to have been latently infected at the time of image acquisition. Random forest models were trained on a spectroscopic signal of noninfected and GLRaV-3 infected grapevines balanced with synthetic minority oversampling of noninfected and GLRaV-3 infected grapevines. The models were able to differentiate between noninfected and GLRaV-3 infected vines both pre- and postsymptomatically at 1 to 5 m resolution. The best-performing models had 87% accuracy distinguishing between noninfected and asymptomatic vines, and 85% accuracy distinguishing between noninfected and asymptomatic + symptomatic vines. The importance of nonvisible wavelengths suggests that this capacity is driven by disease-induced changes to plant physiology. The results lay a foundation for using the forthcoming hyperspectral satellite Surface Biology and Geology for regional disease monitoring in grapevine and other crop species. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
| | - Ryan Pavlick
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
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12
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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13
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Terentev A, Dolzhenko V. Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5366. [PMID: 37420533 PMCID: PMC10302926 DOI: 10.3390/s23125366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/25/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
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14
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Tomaszewski M, Nalepa J, Moliszewska E, Ruszczak B, Smykała K. Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning. Sci Rep 2023; 13:7671. [PMID: 37169807 PMCID: PMC10175501 DOI: 10.1038/s41598-023-34079-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
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Affiliation(s)
- Michał Tomaszewski
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland.
| | - Jakub Nalepa
- Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Ewa Moliszewska
- Faculty of Natural Sciences and Technology, Institute of Environmental Engineering and Biotechnology, University of Opole, Ks. B. Kominka 6a Street, 45-032, Opole, Poland
| | - Bogdan Ruszczak
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Krzysztof Smykała
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- QZ Solutions Sp. z o.o., Ozimska 72A Street, 45-310, Opole, Poland
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15
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Zhou C, Bucklew VG, Edwards PS, Zhang C, Yang J, Ryan PJ, Hughes DP, Qu X, Liu Z. Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease. APPLIED SPECTROSCOPY 2023; 77:491-499. [PMID: 36898969 DOI: 10.1177/00037028231165342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen Phytophthora infestans. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases.
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Affiliation(s)
- Chen Zhou
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | | | | | - Chenji Zhang
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Jinkai Yang
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Philip J Ryan
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - David P Hughes
- Department of Entomology, The Pennsylvania State University, University Park, PA, USA
| | - Xinshun Qu
- Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA, USA
| | - Zhiwen Liu
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, USA
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16
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Li F, Mei DH, Ren T, Song QY. Crude Extracts and Secondary Metabolites of Epichloë bromicola against Phytophthora infestans. Chem Biodivers 2023; 20:e202200841. [PMID: 36471540 DOI: 10.1002/cbdv.202200841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/01/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022]
Abstract
Potato late blight caused by Phytophthora infestans is still one of the main factors limiting potato production. Epichloë spp. can provide host plants with various resistances, which makes them show great potential in the biological control of diseases. In this study, we explored the potential biological activity of crude extracts of 20 strains of Epichloë bromicola to control P. infestans. The crude extracts of strains 1 and 8 showed significant antifungal activity with an inhibition rate of 88 % and 81 %, respectively, and showed different effects on the mycelium morphology of P. infestans observed by scanning electron microscopy. Moreover, the two crude extracts demonstrated an interesting therapeutic and protective effect on potato late blight, and none of the extracts had an adverse effect against zebrafish embryos. A total of 13 metabolites were isolated from the crude extract of strain 8, and these tested compounds showed a weak antifungal effect and the inhibition rate was less than 80 %. These findings suggested that strains 1 and 8 have potential for biocontrol of late potato blight.
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Affiliation(s)
- Fan Li
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China
| | - Da-Hai Mei
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China
| | - Ting Ren
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China
| | - Qiu-Yan Song
- State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, 730020, China
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17
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Berger K, Machwitz M, Kycko M, Kefauver SC, Van Wittenberghe S, Gerhards M, Verrelst J, Atzberger C, van der Tol C, Damm A, Rascher U, Herrmann I, Paz VS, Fahrner S, Pieruschka R, Prikaziuk E, Buchaillot ML, Halabuk A, Celesti M, Koren G, Gormus ET, Rossini M, Foerster M, Siegmann B, Abdelbaki A, Tagliabue G, Hank T, Darvishzadeh R, Aasen H, Garcia M, Pôças I, Bandopadhyay S, Sulis M, Tomelleri E, Rozenstein O, Filchev L, Stancile G, Schlerf M. Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. REMOTE SENSING OF ENVIRONMENT 2022; 280:113198. [PMID: 36090616 PMCID: PMC7613382 DOI: 10.1016/j.rse.2022.113198] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
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Affiliation(s)
- Katja Berger
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Miriam Machwitz
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Marlena Kycko
- Department of Geoinformatics Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, Poland
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Shari Van Wittenberghe
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Max Gerhards
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), University of Valencia, C/Catedrático José Beltrán 2, Paterna 46980, Valencia, Spain
| | - Clement Atzberger
- Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Str. 82, 1190 Vienna, Austria
| | - Christiaan van der Tol
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Alexander Damm
- Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Ittai Herrmann
- The Plant Sensing Laboratory, The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
| | - Veronica Sobejano Paz
- Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Sven Fahrner
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Roland Pieruschka
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Egor Prikaziuk
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Ma. Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028 Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198 Lleida, Spain
| | - Andrej Halabuk
- Institute of Landscape Ecology, Slovak Academy of Sciences, 814 99 Bratislava, Slovakia
| | - Marco Celesti
- HE Space for ESA - European Space Agency, European Space Research and Technology Centre (ESA-ESTEC), Keplerlaan 1, 2201, AZ Noordwijk, the Netherlands
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands
| | - Esra Tunc Gormus
- Department of Geomatics Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Micol Rossini
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Michael Foerster
- Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Asmaa Abdelbaki
- Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany
| | - Giulia Tagliabue
- Remote Sensing of Environmental Dynamics Laboratory (LTDA), University of Milano - Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
| | - Tobias Hank
- Department of Geography, Ludwig-Maximilians-Universität München (LMU), Luisenstr. 37, 80333 Munich, Germany
| | - Roshanak Darvishzadeh
- Faculty Geo-Information Science and Earth Observation, ITC, University of Twente, the Netherlands
| | - Helge Aasen
- Earth Observation and Analysis of Agroecosystems Team, Division Agroecology and Environment, Agroscope, Zurich, Switzerland
- Institute of Agricultural Science, ETH Zürich, Zurich, Switzerland
| | - Monica Garcia
- Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), ETSIAAB, Universidad Politécnica de Madrid, 28040, Spain
| | - Isabel Pôças
- ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, Quinta de Prados, Campus da UTAD, 5001-801 Vila Real, Portugal
| | | | - Mauro Sulis
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
| | - Enrico Tomelleri
- Faculty of Science and Technology, Free University of Bozen/Bolzano, Italy
| | - Offer Rozenstein
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization—Volcani Institute, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
| | - Lachezar Filchev
- Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Bulgaria
| | - Gheorghe Stancile
- National Meteorological Administration, Building A, Soseaua Bucuresti-Ploiesti 97, 013686 Bucuresti, Romania
| | - Martin Schlerf
- Remote Sensing and Natural Resources Modelling Group, Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 41, rue du Brill, L-4422 Belvaux, Luxembourg
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18
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Reis-Pereira M, Tosin R, Martins R, Neves dos Santos F, Tavares F, Cunha M. Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae. PLANTS (BASEL, SWITZERLAND) 2022; 11:2154. [PMID: 36015456 PMCID: PMC9414239 DOI: 10.3390/plants11162154] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/26/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022]
Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.
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Affiliation(s)
- Mafalda Reis-Pereira
- Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Roberto Frias, 4200-465 Porto, Portugal
| | - Renan Tosin
- Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Roberto Frias, 4200-465 Porto, Portugal
| | - Rui Martins
- Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Roberto Frias, 4200-465 Porto, Portugal
| | - Filipe Neves dos Santos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Roberto Frias, 4200-465 Porto, Portugal
| | - Fernando Tavares
- Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal
| | - Mário Cunha
- Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Roberto Frias, 4200-465 Porto, Portugal
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19
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Aoun M, Siegel C, Windham G, Williams W, Nelson R. Application of reflectance spectroscopy to identify maize genotypes and aflatoxin levels in single kernels. WORLD MYCOTOXIN J 2022. [DOI: 10.3920/wmj2021.2750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Spectroscopy is a rapid, non-destructive, and low-cost analytical technique that has the potential to complement more resource-intensive analytical methods. We explored the use of spectral methods to differentiate maize genotypes and assess aflatoxin (AF) contamination in maize kernels. We compared the performance of two instruments: a research-grade ultraviolet-visible-near infrared (UV-Vis-NIR) spectrometer that measures reflectance from 304 -1,085 nm, and a miniaturised NIR spectrometer that measures reflectance from 740-1,070 nm. Both systems were used to predict AF levels in maize kernels from a single genotype and across 10 genotypes, and to predict genotype for the latter. A partial least square discriminant analysis model was trained on 70% of the kernels and tested on the remaining 30%. The classification accuracy for 10 maize genotypes was 71-72% using the UV-Vis-NIR instrument on 1,170 kernels, and 65-66% using the NIR device on 740 kernels. The classification accuracy for 247 AF-contaminated kernels of a single genotype using the UV-Vis-NIR instrument was 71, 82, and 92% for AF thresholds of 20, 100, and 1000 μg/kg, respectively. Using the same spectrometer on 872 kernels from 10 genotypes, AF classification accuracy was 67, 90, and 95% in validation sets for AF thresholds of 20, 100, and 1000 μg/kg, respectively. The UV-Vis-NIR instrument and the NIR device had similar classification accuracies for AF thresholds of 100 and 1000 μg/kg, whereas the NIR device had higher accuracy for the AF threshold of 20 μg/kg. Reflectance spectroscopy outperformed visual sorting and the bright greenish yellow fluorescence test in identifying AF levels. Applying spectral analysis to estimate mycotoxin levels and to identify maize genotypes could contribute to regional toxin surveillance and action efforts. Further, using AF-associated spectral features for grain sorting can reduce AF exposure.
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Affiliation(s)
- M. Aoun
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK 74078, USA
| | - C. Siegel
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - G.L. Windham
- USDA, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Mississippi State, MS 39762, USA
| | - W.P. Williams
- USDA, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Mississippi State, MS 39762, USA
| | - R.J. Nelson
- School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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20
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Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer. REMOTE SENSING 2022. [DOI: 10.3390/rs14122882] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
As a kind of soil-borne epidemic disease, bacterial wilt (BW) is one of the most serious diseases in tomatoes in southern China, which may significantly reduce food quality and the total amount of yield. Hyperspectral remote sensing can detect crop diseases in the early stages and offers potential for BW detection in tomatoes. Tomatoes in southern China are commonly cultivated in greenhouses or bird nets, limiting the application of remote sensing based on natural sunlight. To resolve these issues, we collected the spectrum of tomatoes firstly using the HS-VN1000B Portable Intelligent Spectrometer, which is equipped with a simulated solar light source. We then proposed a tomato BW detection model based on some optimal spectral features. Specifically, these optimal features, including vegetation indexes and principal components (PCs), were extracted by the sequential forward selection (SFS), the simulated annealing (SA), and the genetic algorithm (GA) and were finally fed into the support vector machine (SVM) classifier to detect diseased tomatoes. The results showed that the infected and healthy tomatoes exhibit different spectral characteristics for both leave and stem spectra, especially for near-infrared bands. In addition, the BW detecting model built by the combination of GA and SVM (GA-SVM) achieved the best performance with overall accuracies (OA) of 90.7% for leaves and 92.6% for stems. Compared with the results based on leaves, spectral features of stems provided better accuracy, indicating that the symptom of early infection of BW is more significant in tomato stems than in leaves. Further, the reliability of the GA-SVM tomato stem model was verified in our 2022 experiment with an OA of 88.6% and an F1 score of 0.80. Our study provides an effective means to detect BW disease of tomatoes in the early stages, which could help farmers manage their tomato production and effectively prevent pesticide abuse.
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Nansen C, Imtiaz MS, Mesgaran MB, Lee H. Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects. PLANT METHODS 2022; 18:74. [PMID: 35658997 PMCID: PMC9164469 DOI: 10.1186/s13007-022-00912-z] [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: 12/14/2021] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.
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Affiliation(s)
- Christian Nansen
- Department of Entomology and Nematology, University of California, Davis, USA.
- Department of Entomology and Nematology, UC Davis Briggs Hall, Room 367, Davis, CA, 95616, USA.
| | - Mohammad S Imtiaz
- Department of Electrical & Computer Engineering, Bradley University, Peoria, USA
| | | | - Hyoseok Lee
- Department of Entomology and Nematology, University of California, Davis, USA
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22
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Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, Ustin SL, Wang Z, Wilson AM. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat Ecol Evol 2022; 6:506-519. [PMID: 35332280 DOI: 10.1038/s41559-022-01702-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 02/10/2022] [Indexed: 12/31/2022]
Abstract
Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.
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Affiliation(s)
| | - Fabian D Schneider
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | | | - Amanda Armstrong
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Ana Carnaval
- Department of Biology, Ph.D. Program in Biology, City University of New York and The Graduate Center of CUNY, New York City, NY, USA
| | - Kyla M Dahlin
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
| | - Lola Fatoyinbo
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - George C Hurtt
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - David Schimel
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, Univ. of Wisconsin-Madison, Madison, WI, USA
| | - Susan L Ustin
- Department of Land, Air and Water Resources and the John Muir Institute of the Environment, University of California, Davis, CA, USA
| | - Zhihui Wang
- Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China
| | - Adam M Wilson
- Department of Geography, University at Buffalo, Buffalo, NY, USA
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23
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Kuska MT, Heim RHJ, Geedicke I, Gold KM, Brugger A, Paulus S. Digital plant pathology: a foundation and guide to modern agriculture. JOURNAL OF PLANT DISEASES AND PROTECTION : SCIENTIFIC JOURNAL OF THE GERMAN PHYTOMEDICAL SOCIETY (DPG) 2022; 129:457-468. [PMID: 35502325 PMCID: PMC9046714 DOI: 10.1007/s41348-022-00600-z] [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: 02/24/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.
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Affiliation(s)
- Matheus Thomas Kuska
- North Rhine-Westphalia Chamber of Agriculture, Gartenstraße 11, 50765 Cologne, Germany
| | - René H. J. Heim
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Ina Geedicke
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
| | - Kaitlin M. Gold
- Plant Pathology and Plant-Microbe Biology College of Agriculture and Life Science, Cornell University, Cornell AgriTech, 15 Castle Creek Drive, Geneva, 14456 USA
| | - Anna Brugger
- Bildungs- und Beratungszentrum Arenenberg, Arenenberg 8, 8268 Salenstein, Switzerland
| | - Stefan Paulus
- Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079 Göttingen, Germany
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24
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Surveying soil-borne disease development on wild rocket salad crop by proximal sensing based on high-resolution hyperspectral features. Sci Rep 2022; 12:5098. [PMID: 35332172 PMCID: PMC8948195 DOI: 10.1038/s41598-022-08969-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 03/14/2022] [Indexed: 11/08/2022] Open
Abstract
Wild rocket (Diplotaxis tenuifolia, Brassicaceae) is a baby-leaf vegetable crop of high economic interest, used in ready-to-eat minimally processed salads, with an appreciated taste and nutraceutical features. Disease management is key to achieving the sustainability of the entire production chain in intensive systems, where synthetic fungicides are limited or not permitted. In this context, soil-borne pathologies, much feared by growers, are becoming a real emergency. Digital screening of green beds can be implemented in order to optimize the use of sustainable means. The current study used a high-resolution hyperspectral array (spectroscopy at 350-2500 nm) to attempt to follow the progression of symptoms of Rhizoctonia, Sclerotinia, and Sclerotium disease across four different severity levels. A Random Forest machine learning model reduced dimensions of the training big dataset allowing to compute de novo vegetation indices specifically informative about canopy decay caused by all basal pathogenic attacks. Their transferability was also tested on the canopy dataset, which was useful for assessing the health status of wild rocket plants. Indeed, the progression of symptoms associated with soil-borne pathogens is closely related to the reduction of leaf absorbance of the canopy in certain ranges of visible and shortwave infrared spectral regions sensitive to reduction of chlorophyll and other pigments as well as to modifications of water content and turgor.
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25
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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26
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Terentev A, Dolzhenko V, Fedotov A, Eremenko D. Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review. SENSORS 2022; 22:s22030757. [PMID: 35161504 PMCID: PMC8839015 DOI: 10.3390/s22030757] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 01/10/2023]
Abstract
The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 3 Podbelsokogo Str., Pushkin, 196608 Saint Petersburg, Russia;
| | - Alexander Fedotov
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
- Correspondence: (A.T.); (A.F.); Tel.: +7-921-937-1550 (A.T.); +7-921-741-6303 (A.F.)
| | - Danila Eremenko
- World-Class Research Center «Advanced Digital Technologies», Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya Str., 195251 Saint Petersburg, Russia;
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27
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Ai HL, Shi BB, Li W, He J, Li ZH, Feng T, Liu JK. Bipolarithizole A, an antifungal phenylthiazole-sativene merosesquiterpenoid from the potato endophytic fungus Bipolaris eleusines. Org Chem Front 2022. [DOI: 10.1039/d1qo01887f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Bipolarithizole A (1) is a phenylthiazole-sativene sesquiterpenoid hybrid isolated from the fungus Bipolaris eleusines. It shows anti-pathogenic fungi activity against Rhizoctonia solani with an MIC value of 16 μg mL−1.
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Affiliation(s)
- Hong-Lian Ai
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Bao-Bao Shi
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Wei Li
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Juan He
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Zheng-Hui Li
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Tao Feng
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Ji-Kai Liu
- School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China
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28
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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Abstract
Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.
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30
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Blank V, Skidanov R, Doskolovich L, Kazanskiy N. Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index. SENSORS 2021; 21:s21227694. [PMID: 34833770 PMCID: PMC8622293 DOI: 10.3390/s21227694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/11/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022]
Abstract
We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and -1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.
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Affiliation(s)
- Veronika Blank
- Image Processing Systems Institute of RAS—Branch of the Federal Scientific Research Centre Crystallography and Photonics of Russian Academy of Sciences, 151 Molodogvardeyskaya St., 443001 Samara, Russia; (V.B.); (L.D.); (N.K.)
- Department of Technical Cybernetics, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
| | - Roman Skidanov
- Image Processing Systems Institute of RAS—Branch of the Federal Scientific Research Centre Crystallography and Photonics of Russian Academy of Sciences, 151 Molodogvardeyskaya St., 443001 Samara, Russia; (V.B.); (L.D.); (N.K.)
- Department of Technical Cybernetics, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
- Correspondence:
| | - Leonid Doskolovich
- Image Processing Systems Institute of RAS—Branch of the Federal Scientific Research Centre Crystallography and Photonics of Russian Academy of Sciences, 151 Molodogvardeyskaya St., 443001 Samara, Russia; (V.B.); (L.D.); (N.K.)
| | - Nikolay Kazanskiy
- Image Processing Systems Institute of RAS—Branch of the Federal Scientific Research Centre Crystallography and Photonics of Russian Academy of Sciences, 151 Molodogvardeyskaya St., 443001 Samara, Russia; (V.B.); (L.D.); (N.K.)
- Department of Technical Cybernetics, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
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Burnett AC, Serbin SP, Davidson KJ, Ely KS, Rogers A. Detection of the metabolic response to drought stress using hyperspectral reflectance. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6474-6489. [PMID: 34235536 DOI: 10.1093/jxb/erab255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/02/2021] [Indexed: 06/13/2023]
Abstract
Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
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Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kenneth J Davidson
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kim S Ely
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
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32
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Near infrared spectroscopy to rapid assess the rubber tree clone and the influence of maturation and disease at the leaves. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106478] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Jindo K, Evenhuis A, Kempenaar C, Pombo Sudré C, Zhan X, Goitom Teklu M, Kessel G. Review: Holistic pest management against early blight disease towards sustainable agriculture. PEST MANAGEMENT SCIENCE 2021; 77:3871-3880. [PMID: 33538396 PMCID: PMC8451811 DOI: 10.1002/ps.6320] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/22/2021] [Accepted: 02/04/2021] [Indexed: 05/24/2023]
Abstract
Alternaria species are well-known aggressive pathogens that are widespread globally and warmer temperatures caused by climate change might increase their abundance more drastically. Early blight (EB) disease, caused mainly by Alternaria solani, and brown spot, caused by Alternaria alternata, are major concerns in potato, tomato and eggplant production. The development of EB is strongly linked to varieties, crop development stages, environmental factors, cultivation and field management. Several forecasting models for pesticide application to control EB were created in the last century and more recent scientific advances have included modern breeding technology to detect resistant genes and precision agriculture with hyperspectral sensors to pinpoint damage locations on plants. This paper presents an overview of the EB disease and provides an evaluation of recent scientific advances to control the disease. First of all, we describe the outline of this disease, encompassing biological cycles of the Alternaria genus, favorite climate and soil conditions as well as resistant plant species. Second, versatile management practices to minimize the effect of this pathogen at field level are discussed, covering their limitations and pitfalls. A better understanding of the underlying factors of this disease and the potential of novel research can contribute to implementing integrated pest management systems for an ecofriendly farming system. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Keiji Jindo
- Agrosystems ResearchWageningen University & ResearchWageningenThe Netherlands
| | | | - Corné Kempenaar
- Agrosystems ResearchWageningen University & ResearchWageningenThe Netherlands
| | - Cláudia Pombo Sudré
- Laboratório de Melhoramento Genético VegetalUniversidade Estadual do Norte Fluminense Darcy Ribeiro, UENFCampos dos GoytacazesBrazil
| | - Xiaoxiu Zhan
- Department of Crop Cultivation and Farming SystemCollege of Agronomy, Sichuan Agricultural UniversityChengduChina
| | | | - Geert Kessel
- Field CropsWageningen University & ResearchLelystadThe Netherlands
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34
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Burnett AC, Serbin SP, Rogers A. Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop. PLANT, CELL & ENVIRONMENT 2021; 44:2466-2479. [PMID: 33764536 DOI: 10.1111/pce.14056] [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: 02/02/2021] [Revised: 03/23/2021] [Accepted: 03/23/2021] [Indexed: 05/21/2023]
Abstract
The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R2 values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R2 values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R2 was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.
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Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
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Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13142833] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems.
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Silva G, Tomlinson J, Onkokesung N, Sommer S, Mrisho L, Legg J, Adams IP, Gutierrez-Vazquez Y, Howard TP, Laverick A, Hossain O, Wei Q, Gold KM, Boonham N. Plant pest surveillance: from satellites to molecules. Emerg Top Life Sci 2021; 5:275-287. [PMID: 33720345 PMCID: PMC8166340 DOI: 10.1042/etls20200300] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 11/18/2022]
Abstract
Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.
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Affiliation(s)
- Gonçalo Silva
- Natural Resources Institute, University of Greenwich, Central Avenue, Chatham Maritime, Kent ME4 4TB, U.K
| | - Jenny Tomlinson
- Fera Science Ltd., York Biotech Campus, Sand Hutton, York YO41 1LZ, U.K
| | - Nawaporn Onkokesung
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Sarah Sommer
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Latifa Mrisho
- International Institute of Tropical Agriculture, Dar el Salaam, Tanzania
| | - James Legg
- International Institute of Tropical Agriculture, Dar el Salaam, Tanzania
| | - Ian P Adams
- Fera Science Ltd., York Biotech Campus, Sand Hutton, York YO41 1LZ, U.K
| | | | - Thomas P Howard
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Alex Laverick
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
| | - Oindrila Hossain
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A
| | - Qingshan Wei
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695, U.S.A
| | - Kaitlin M Gold
- Plant Pathology and Plant Microbe Biology Section, Cornell University, 15 Castle Creek Drive, Geneva, NY 14456, U.S.A
| | - Neil Boonham
- School of Natural and Environmental Sciences, Agriculture Building, Newcastle University, King's Road, Newcastle upon Tyne NE1 7RU, U.K
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DeLaMater DS, Couture JJ, Puzey JR, Dalgleish HJ. Range-wide variations in common milkweed traits and their effect on monarch larvae. AMERICAN JOURNAL OF BOTANY 2021; 108:388-401. [PMID: 33792047 DOI: 10.1002/ajb2.1630] [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: 07/02/2020] [Accepted: 10/08/2020] [Indexed: 06/12/2023]
Abstract
PREMISE Leaf economic spectrum (LES) theory has historically been employed to inform vegetation models of ecosystem processes, but largely neglects intraspecific variation and biotic interactions. We attempt to integrate across environment-plant trait-herbivore interactions within a species at a range-wide scale. METHODS We measured traits in 53 populations spanning the range of common milkweed (Asclepias syriaca) and used a common garden to determine the role of environment in driving patterns of intraspecific variation. We used a feeding trial to determine the role of plant traits in monarch (Danaus plexippus) larval development. RESULTS Trait-trait relationships largely followed interspecific patterns in LES theory and persisted in a common garden when individual traits change. Common milkweed showed intraspecific variation and biogeographic clines in traits. Clines did not persist in a common garden. Larvae ate more and grew larger when fed plants with more nitrogen. A longitudinal environmental gradient in precipitation corresponded to a resource gradient in plant nitrogen, which produces a gradient in larval performance. CONCLUSIONS Biogeographic patterns in common milkweed traits can sometimes be predicted from LES, are largely driven by environmental conditions, and have consequences for monarch larval performance. Changes to nutrient dynamics of landscapes with common milkweed could potentially influence monarch population dynamics. We show how biogeographic trends in intraspecific variation can influence key ecological interactions, especially in common species with large distributions.
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Affiliation(s)
- David S DeLaMater
- Department of Biology, William & Mary, 540 Landrum Drive, Williamsburg, VA, 23185, USA
| | - John J Couture
- Departments of Entomology and Forestry and Natural Resources, Purdue University, 170 S. University Street, West Lafayette, IN, 47907, USA
| | - Joshua R Puzey
- Department of Biology, William & Mary, 540 Landrum Drive, Williamsburg, VA, 23185, USA
| | - Harmony J Dalgleish
- Department of Biology, William & Mary, 540 Landrum Drive, Williamsburg, VA, 23185, USA
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Gao J, Westergaard JC, Sundmark EHR, Bagge M, Liljeroth E, Alexandersson E. Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106723] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Castrignanò A, Belmonte A, Antelmi I, Quarto R, Quarto F, Shaddad S, Sion V, Muolo MR, Ranieri NA, Gadaleta G, Bartoccetti E, Riefolo C, Ruggieri S, Nigro F. A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 752:141814. [PMID: 32890831 DOI: 10.1016/j.scitotenv.2020.141814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 06/11/2023]
Abstract
Xylella fastidiosa is one of the most destructive plant pathogenic bacteria worldwide, affecting more than 500 plant species. In Apulia region (southeastern Italy), X. fastidiosa subsp. pauca (Xfp) is responsible for a severe disease, the olive quick decline syndrome (OQDS), spreading epidemically and with dramatic impact on the agriculture, the landscape, the tourism, and the cultural heritage of this region. An early detection of the infected plants would hinder the rapid spread of the disease. The main objective of this paper was to define a geostatistical approach of data fusion, which combines remote (radiometric), and proximal (geophysical) sensor data and visual inspections with plant diagnostic tests, to provide probabilistic maps of Xfp infection risk. The study site was an olive grove located at Oria (province of Brindisi, Italy), where at the time of monitoring (September 2017) only few plants showed initial symptoms of the disease. The measurements included: 1) acquisitions of reflected electromagnetic radiation with UAV (Unmanned Aerial Vehicle) equipped with a multi-spectral camera; 2) geophysical surveys on the trunks of 49 plants with Ground Penetrating Radar (GPR); 3) disease severity rating, by visual inspection of the proportion of canopy with symptoms; 4) qPCR (real time-quantitative Polymerase Chain Reaction) data from tests on 61 plants. The data were submitted to a set of processing techniques to define a "data fusion" procedure, based on non-parametric multivariate geostatistics. The approach allowed marking those areas where the risk of infection was higher, and identifying the possible infection entry routes into the field. The probability map of infection risk could be used as an effective tool for a preventive action and for a better organization of the monitoring plans.
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Affiliation(s)
- Annamaria Castrignanò
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Antonella Belmonte
- CNR-IREA National Research Council - Institute for Electromagnetic Sensing of the Environment (Bari, Italy), Via Amendola, 122/D, 70126 Bari, Italy.
| | - Ilaria Antelmi
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
| | - Ruggiero Quarto
- Department of Earth and Geo-Environmental Sciences, University of Bari, Via Edoardo Orabona, 4, 70125 Bari (BA), Italy
| | - Francesco Quarto
- PRO-GEO s.a.s, Via M. R. Imbriani 13, 76121 Barletta (BT), Italy
| | - Sameh Shaddad
- Soil science Department, Faculty of Agriculture, Zagazig University, 44511 Zagazig, Egypt
| | - Valentina Sion
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
| | - Maria Rita Muolo
- Servizi di Informazione Territoriale S.r.l., Piazza Giovanni Paolo II, 8, 70015 Noci (BA), Italy
| | - Nicola A Ranieri
- Servizi di Informazione Territoriale S.r.l., Piazza Giovanni Paolo II, 8, 70015 Noci (BA), Italy
| | - Giovanni Gadaleta
- Professional Agronomist, Via Carr. Lamaveta, 63/F, 76011 Bisceglie (BT), Italy
| | | | - Carmela Riefolo
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Sergio Ruggieri
- CREA-AA - Council for Agricultural Research and Economics (Bari, Italy), Via Celso Ulpiani, 5, 70125 Bari (BA), Italy
| | - Franco Nigro
- Department of Soil, Plant and Food Sciences, University of Bari - Aldo Moro, Via G. Amendola 165/A, 70126 Bari, Italy
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Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes. REMOTE SENSING 2020. [DOI: 10.3390/rs12244041] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Finding trees that are resistant to pathogens is key in preparing for current and future disease threats such as the invasive white pine blister rust. In this study, we analyzed the potential of using hyperspectral imaging to find and diagnose the degree of infection of the non-native white pine blister rust in southwestern white pine seedlings from different seed-source families. A support vector machine was able to automatically detect infection with a classification accuracy of 87% (κ = 0.75) over 16 image collection dates. Hyperspectral imaging only missed 4% of infected seedlings that were impacted in terms of vigor according to expert’s assessments. Classification accuracy per family was highly correlated with mortality rate within a family. Moreover, classifying seedlings into a ‘growth vigor’ grouping used to identify the degree of impact of the disease was possible with 79.7% (κ = 0.69) accuracy. We ranked hyperspectral features for their importance in both classification tasks using the following features: 84 vegetation indices, simple ratios, normalized difference indices, and first derivatives. The most informative features were identified using a ‘new search algorithm’ that combines both the p-value of a 2-sample t-test and the Bhattacharyya distance. We ranked the normalized photochemical reflectance index (PRIn) first for infection detection. This index also had the highest classification accuracy (83.6%). Indices such as PRIn use only a small subset of the reflectance bands. This could be used for future developments of less expensive and more data-parsimonious multispectral cameras.
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Stackhouse T, Martinez-Espinoza AD, Ali ME. Turfgrass Disease Diagnosis: Past, Present, and Future. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1544. [PMID: 33187303 PMCID: PMC7697262 DOI: 10.3390/plants9111544] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/30/2020] [Accepted: 11/09/2020] [Indexed: 01/15/2023]
Abstract
Turfgrass is a multibillion-dollar industry severely affected by plant pathogens including fungi, bacteria, viruses, and nematodes. Many of the diseases in turfgrass have similar signs and symptoms, making it difficult to diagnose the specific problem pathogen. Incorrect diagnosis leads to the delay of treatment and excessive use of chemicals. To effectively control these diseases, it is important to have rapid and accurate detection systems in the early stages of infection that harbor relatively low pathogen populations. There are many methods for diagnosing pathogens on turfgrass. Traditional methods include symptoms, morphology, and microscopy identification. These have been followed by nucleic acid detection and onsite detection techniques. Many of these methods allow for rapid diagnosis, some even within the field without much expertise. There are several methods that have great potential, such as high-throughput sequencing and remote sensing. Utilization of these techniques for disease diagnosis allows for faster and accurate disease diagnosis and a reduction in damage and cost of control. Understanding of each of these techniques can allow researchers to select which method is best suited for their pathogen of interest. The objective of this article is to provide an overview of the turfgrass diagnostics efforts used and highlight prospects for disease detection.
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Affiliation(s)
- Tammy Stackhouse
- Department of Plant Pathology, University of Georgia, Tifton, GA 31793, USA;
| | | | - Md Emran Ali
- Department of Plant Pathology, University of Georgia, Tifton, GA 31793, USA;
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A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery. REMOTE SENSING 2020. [DOI: 10.3390/rs12213621] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.
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Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification. REMOTE SENSING 2020. [DOI: 10.3390/rs12193233] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.
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Crandall SG, Gold KM, Jiménez-Gasco MDM, Filgueiras CC, Willett DS. A multi-omics approach to solving problems in plant disease ecology. PLoS One 2020; 15:e0237975. [PMID: 32960892 PMCID: PMC7508392 DOI: 10.1371/journal.pone.0237975] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/04/2020] [Indexed: 12/11/2022] Open
Abstract
The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.
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Affiliation(s)
- Sharifa G. Crandall
- Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA, United States of America
| | - Kaitlin M. Gold
- Plant Pathology & Plant Microbe Biology Section, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
| | - María del Mar Jiménez-Gasco
- Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA, United States of America
| | - Camila C. Filgueiras
- Applied Chemical Ecology Technology, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
| | - Denis S. Willett
- Applied Chemical Ecology Technology, Cornell AgriTech, Cornell University, Geneva, NY, United States of America
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Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy. REMOTE SENSING 2020. [DOI: 10.3390/rs12121920] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%.
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Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions. REMOTE SENSING 2020. [DOI: 10.3390/rs12081292] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Potato late blight, caused by Phytophthora infestans, is a major disease worldwide that has a significant economic impact on potato crops, and remote sensing might help to detect the disease in early stages. This study aims to determine changes induced by potato late blight in two parameters of the red and red-edge spectral regions: the red-well point (RWP) and the red-edge point (REP) as a function of the number of days post-inoculation (DPI) at the leaf and canopy levels. The RWP or REP variations were modelled using linear or exponential regression models as a function of the DPI. A Support Vector Machine (SVM) algorithm was used to classify healthy and infected leaves or plants using either the RWP or REP wavelength as well as the reflectances at 668, 705, 717 and 740 nm. Higher variations in the RWP and REP wavelengths were observed for the infected leaves compared to healthy leaves. The linear and exponential models resulted in higher adjusted R2 for the infected case than for the healthy case. The SVM classifier applied to the reflectance of the red and red-edge bands of the Micasense® Dual-X camera was able to sort healthy and infected cases with both the leaf and canopy measurements, reaching an overall classification accuracy of 89.33% at 3 DPI when symptoms were visible for the first time with the leaf measurements and of 89.06% at 5 DPI, i.e., two days after the symptoms became apparent, with the canopy measurements. The study shows that RWP and REP at leaf and canopy levels allow detecting potato late blight, but these parameters are less efficient to sort healthy and infected leaves or plants than the reflectance at 668, 705, 717 and 740 nm. Future research should consider larger samples, other cultivars and the test of unmanned aerial vehicle (UAV) imagery for field-based detection.
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