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Bai Y, Feng M, Zhao J, Wang J, Ke Q, Zhou J, Jiang P, Chen S, Chen L, Liu W, Jiang T, Li Y, Tian G, Zhou T, Xu P. Machine vision-assisted genomic prediction and genome-wide association of spleen-related traits in large yellow croaker infected with visceral white-nodules disease. FISH & SHELLFISH IMMUNOLOGY 2024:109948. [PMID: 39384056 DOI: 10.1016/j.fsi.2024.109948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 10/01/2024] [Accepted: 10/05/2024] [Indexed: 10/11/2024]
Abstract
High-resolution and high-throughput genotype-to-phenotype studies in fish are rapidly advancing, driven by innovative technologies that aim to address the challenges of modern breeding models. In recent years, machine vision and deep learning techniques, particularly convolutional neural networks (CNNs), have achieved significant success in image recognition and segmentation. Moreover, qualitative and quantitative analysis of disease resistance has always been a crucial field of research in genetics. This motivation has led us to investigate the potential of large yellow croaker visceral white-nodules disease (VWND) in encoding information on disease resistance for the task of accession classification. In this study, we proposed an image segmentation framework for the feature extraction of the spleen after VWND infection based on machine vision. We utilized deep CNNs and threshold segmentation for automatic feature learning and object segmentation. This approach eliminates subjectivity and enhances work efficiency compared to using hand-crafted features. Additionally, we employed spleen-related traits to conduct genome-wide association analysis (GWAS), which led to the identification of 24 significant SNPs and 10 major quantitative trait loci. The results of function enrichment analysis on candidate genes also indicated potential relationships with immune regulation mechanisms. Furthermore, we explored the use of genomic selection (GS) technology for phenotype prediction of extreme individuals, which further supports the predictability of spleen-related phenotypes for VWND resistance in large yellow croakers. Our findings demonstrate that artificial intelligence (AI)-based phenotyping approaches can deliver state-of-the-art performance for genetics research. We hope this work will provide a paradigm for applying deep learning and machine vision to phenotyping in aquaculture species.
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Affiliation(s)
- Yulin Bai
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Miaosheng Feng
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Ji Zhao
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China; State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Jiaying Wang
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Qiaozhen Ke
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China; State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Jiang Zhou
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Pengxin Jiang
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Sijing Chen
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Longyu Chen
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Wei Liu
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Tingsen Jiang
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Yichen Li
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China
| | - Guopeng Tian
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China
| | - Tao Zhou
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China
| | - Peng Xu
- State Key Laboratory of Mariculture Breeding, Xiamen University, Xiamen 361102, China; Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China.
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Einspanier S, Tominello-Ramirez C, Hasler M, Barbacci A, Raffaele S, Stam R. High-Resolution Disease Phenotyping Reveals Distinct Resistance Mechanisms of Tomato Crop Wild Relatives against Sclerotinia sclerotiorum. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0214. [PMID: 39105186 PMCID: PMC11298253 DOI: 10.34133/plantphenomics.0214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 06/19/2024] [Indexed: 08/07/2024]
Abstract
Besides the well-understood qualitative disease resistance, plants possess a more complex quantitative form of resistance: quantitative disease resistance (QDR). QDR is commonly defined as a partial but more durable form of resistance and, therefore, might display a valuable target for resistance breeding. The characterization of QDR phenotypes, especially of wild crop relatives, displays a bottleneck in deciphering QDR's genomic and regulatory background. Moreover, the relationship between QDR parameters, such as infection frequency, lag-phase duration, and lesion growth rate, remains elusive. High hurdles for applying modern phenotyping technology, such as the low availability of phenotyping facilities or complex data analysis, further dampen progress in understanding QDR. Here, we applied a low-cost (<1.000 €) phenotyping system to measure lesion growth dynamics of wild tomato species (e.g., Solanum pennellii or Solanum pimpinellifolium). We provide insight into QDR diversity of wild populations and derive specific QDR mechanisms and their cross-talk. We show how temporally continuous observations are required to dissect end-point severity into functional resistance mechanisms. The results of our study show how QDR can be maintained by facilitating different defense mechanisms during host-parasite interaction and that the capacity of the QDR toolbox highly depends on the host's genetic context. We anticipate that the present findings display a valuable resource for more targeted functional characterization of the processes involved in QDR. Moreover, we show how modest phenotyping technology can be leveraged to help answer highly relevant biological questions.
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Affiliation(s)
- Severin Einspanier
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Christopher Tominello-Ramirez
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
| | - Mario Hasler
- Lehrfach Variationsstatistik, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, Kiel, 24118 Kiel, Germany
| | - Adelin Barbacci
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Sylvain Raffaele
- Laboratoire des Interactions Plantes Microorganismes Environnement (LIPME), INRAE, CNRS, Castanet Tolosan Cedex, France
| | - Remco Stam
- Department of Phytopathology and Crop Protection, Institute of Phytopathology, Faculty of Agricultural and Nutritional Sciences,
Christian-Albrechts-University, 24118 Kiel, Germany
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Nsibo DL, Barnes I, Berger DK. Recent advances in the population biology and management of maize foliar fungal pathogens Exserohilum turcicum, Cercospora zeina and Bipolaris maydis in Africa. FRONTIERS IN PLANT SCIENCE 2024; 15:1404483. [PMID: 39148617 PMCID: PMC11324496 DOI: 10.3389/fpls.2024.1404483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 07/01/2024] [Indexed: 08/17/2024]
Abstract
Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum, Cercospora zeina, and Bipolaris maydis, respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent.
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Affiliation(s)
- David L Nsibo
- Department of Plant and Soil Sciences, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa
| | - Irene Barnes
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa
| | - Dave K Berger
- Department of Plant and Soil Sciences, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa
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Thapa S, Gill HS, Halder J, Rana A, Ali S, Maimaitijiang M, Gill U, Bernardo A, St Amand P, Bai G, Sehgal SK. Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat. THE PLANT GENOME 2024:e20470. [PMID: 38853339 DOI: 10.1002/tpg2.20470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/07/2024] [Accepted: 04/14/2024] [Indexed: 06/11/2024]
Abstract
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.
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Affiliation(s)
- Subash Thapa
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Harsimardeep S Gill
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Jyotirmoy Halder
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Anshul Rana
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Shaukat Ali
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
| | - Maitiniyazi Maimaitijiang
- Department of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, South Dakota, USA
| | - Upinder Gill
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, USA
| | - Amy Bernardo
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Paul St Amand
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Guihua Bai
- USDA-ARS, Hard Winter Wheat Genetics Research Unit, Manhattan, Kansas, USA
| | - Sunish K Sehgal
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, South Dakota, USA
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Maina AW, Oerke EC. Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes. PLANT METHODS 2024; 20:87. [PMID: 38849955 PMCID: PMC11161989 DOI: 10.1186/s13007-024-01215-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 05/29/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450-850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen. RESULTS M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data. CONCLUSIONS HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.
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Affiliation(s)
- Angeline Wanjiku Maina
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany.
| | - Erich-Christian Oerke
- Institute for Crop Science and Resource Conservation (INRES) - Plant Pathology, Rheinische Friedrich-Wilhelms University of Bonn, Bonn, Germany
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Catalano C, Di Guardo M, Licciardello G, Seminara S, Tropea Garzia G, Biondi A, Troggio M, Bianco L, La Malfa S, Gentile A, Distefano G. QTL analysis on a lemon population provides novel insights on the genetic regulation of the tolerance to the two-spotted spider mite attack. BMC PLANT BIOLOGY 2024; 24:509. [PMID: 38844865 PMCID: PMC11157791 DOI: 10.1186/s12870-024-05211-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 05/28/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Among the Citrus species, lemon (Citrus limon Burm f.) is one of the most affected by the two-spotted spider mite (Tetranychus urticae Koch). Moreover, chemical control is hampered by the mite's ability to develop genetic resistance against acaricides. In this context, the identification of the genetic basis of the host resistance could represent a sustainable strategy for spider mite control. In the present study, a marker-trait association analysis was performed on a lemon population employing an association mapping approach. An inter-specific full-sib population composed of 109 accessions was phenotyped through a detached-leaf assays performed in modified Huffaker cells. Those individuals, complemented with two inter-specific segregating populations, were genotyped using a target-sequencing approach called SPET (Single Primer Enrichment Technology), the resulting SNPs were employed for the generation of an integrated genetic map. RESULTS The percentage of damaged area in the full-sib population showed a quantitative distribution with values ranging from 0.36 to 9.67%. A total of 47,298 SNPs were selected for an association mapping study and a significant marker linked with resistance to spider mite was detected on linkage group 5. In silico gene annotation of the QTL interval enabled the detection of 13 genes involved in immune response to biotic and abiotic stress. Gene expression analysis showed an over expression of the gene encoding for the ethylene-responsive transcription factor ERF098-like, already characterized in Arabidopsis and in rice for its involvement in defense response. CONCLUSION The identification of a molecular marker linked to the resistance to spider mite attack can pave the way for the development of marker-assisted breeding plan for the development of novel selection coupling favorable agronomical traits (e.g. fruit quality, yield) with a higher resistance toward the mite.
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Affiliation(s)
- Chiara Catalano
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Mario Di Guardo
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Giuliana Licciardello
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Sebastiano Seminara
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Giovanna Tropea Garzia
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy.
| | - Antonio Biondi
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Michela Troggio
- Research and Innovation Centre, San Michele All' Adige, Fondazione Edmund Mach, Trento, Italy
| | - Luca Bianco
- Research and Innovation Centre, San Michele All' Adige, Fondazione Edmund Mach, Trento, Italy
| | - Stefano La Malfa
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
| | - Alessandra Gentile
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy.
| | - Gaetano Distefano
- Department of Agriculture, Food and Environment, University of Catania, Via Santa Sofia 100, Catania, 95123, Italy
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Paauw M, Hardeman G, Taks NW, Lambalk L, Berg JA, Pfeilmeier S, van den Burg HA. ScAnalyzer: an image processing tool to monitor plant disease symptoms and pathogen spread in Arabidopsis thaliana leaves. PLANT METHODS 2024; 20:80. [PMID: 38822355 PMCID: PMC11141064 DOI: 10.1186/s13007-024-01213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/25/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Plants are known to be infected by a wide range of pathogenic microbes. To study plant diseases caused by microbes, it is imperative to be able to monitor disease symptoms and microbial colonization in a quantitative and objective manner. In contrast to more traditional measures that use manual assignments of disease categories, image processing provides a more accurate and objective quantification of plant disease symptoms. Besides monitoring disease symptoms, computational image processing provides additional information on the spatial localization of pathogenic microbes in different plant tissues. RESULTS Here we report on an image analysis tool called ScAnalyzer to monitor disease symptoms and bacterial spread in Arabidopsis thaliana leaves. Thereto, detached leaves are assembled in a grid and scanned, which enables automated separation of individual samples. A pixel color threshold is used to segment healthy (green) from chlorotic (yellow) leaf areas. The spread of luminescence-tagged bacteria is monitored via light-sensitive films, which are processed in a similar manner as the leaf scans. We show that this tool is able to capture previously identified differences in susceptibility of the model plant A. thaliana to the bacterial pathogen Xanthomonas campestris pv. campestris. Moreover, we show that the ScAnalyzer pipeline provides a more detailed assessment of bacterial spread within plant leaves than previously used methods. Finally, by combining the disease symptom values with bacterial spread values from the same leaves, we show that bacterial spread precedes visual disease symptoms. CONCLUSION Taken together, we present an automated script to monitor plant disease symptoms and microbial spread in A. thaliana leaves. The freely available software ( https://github.com/MolPlantPathology/ScAnalyzer ) has the potential to standardize the analysis of disease assays between different groups.
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Affiliation(s)
- Misha Paauw
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Gerrit Hardeman
- Technologie Centrum FNWI, Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Nanne W Taks
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Lennart Lambalk
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Jeroen A Berg
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Sebastian Pfeilmeier
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands
| | - Harrold A van den Burg
- Molecular Plant Pathology, Faculty of Science, Swammerdam Institute for Life Sciences (SILS), University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, The Netherlands.
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Holan KL, White CH, Whitham SA. Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem. PHYTOPATHOLOGY 2024; 114:990-999. [PMID: 38281155 DOI: 10.1094/phyto-09-23-0313-kc] [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: 01/30/2024]
Abstract
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [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)
- Katerina L Holan
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014
| | - Charles H White
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523
| | - Steven A Whitham
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014
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Lee JH, Lee U, Yoo JH, Lee TS, Jung JH, Kim HS. AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings. PLANT METHODS 2024; 20:44. [PMID: 38493119 PMCID: PMC10943777 DOI: 10.1186/s13007-024-01171-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.
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Grants
- Grant No. 2022R1C1C1012137 The National Research Foundation of Korea
- Grant No. 421002-04) The Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) and Korea Smart Farm R&D (KosFarm) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA)
- The Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) and Korea Smart Farm R&D (KosFarm) through the Smart Farm Innovation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA)
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Affiliation(s)
- Jae Hoon Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
- Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Unseok Lee
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Ji Hye Yoo
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Taek Sung Lee
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Je Hyeong Jung
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Hyoung Seok Kim
- Smart Farm Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.
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10
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Hudson A, Mullens A, Hind S, Jamann T, Balint-Kurti P. Natural variation in the pattern-triggered immunity response in plants: Investigations, implications and applications. MOLECULAR PLANT PATHOLOGY 2024; 25:e13445. [PMID: 38528659 DOI: 10.1111/mpp.13445] [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: 12/29/2023] [Revised: 02/26/2024] [Accepted: 03/01/2024] [Indexed: 03/27/2024]
Abstract
The pattern-triggered immunity (PTI) response is triggered at the plant cell surface by the recognition of microbe-derived molecules known as microbe- or pathogen-associated molecular patterns or molecules derived from compromised host cells called damage-associated molecular patterns. Membrane-localized receptor proteins, known as pattern recognition receptors, are responsible for this recognition. Although much of the machinery of PTI is conserved, natural variation for the PTI response exists within and across species with respect to the components responsible for pattern recognition, activation of the response, and the strength of the response induced. This review describes what is known about this variation. We discuss how variation in the PTI response can be measured and how this knowledge might be utilized in the control of plant disease and in developing plant varieties with enhanced disease resistance.
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Affiliation(s)
- Asher Hudson
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexander Mullens
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Sarah Hind
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Tiffany Jamann
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Peter Balint-Kurti
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, North Carolina, USA
- Plant Science Research Unit, USDA-ARS, Raleigh, North Carolina, USA
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11
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Farokhzad S, Modaress Motlagh A, Ahmadi Moghaddam P, Jalali Honarmand S, Kheiralipour K. A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing. Sci Rep 2024; 14:1995. [PMID: 38263218 PMCID: PMC10805740 DOI: 10.1038/s41598-023-50948-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/28/2023] [Indexed: 01/25/2024] Open
Abstract
This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu's threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers.
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Affiliation(s)
- Saeid Farokhzad
- Department of Mechanical Biosystems, Faculty of Agriculture, Urmia University, Urmia, Iran.
| | - Asad Modaress Motlagh
- Department of Mechanical Biosystems, Faculty of Agriculture, Urmia University, Urmia, Iran.
| | | | - Saeid Jalali Honarmand
- Department of Agronomy and Plant Breeding, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran
| | - Kamran Kheiralipour
- Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Ilam University, Ilam, Iran.
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12
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Lawson T, Vialet-Chabrand S. Imaging Spatial and Temporal Variation in Photosynthesis Using Chlorophyll Fluorescence. Methods Mol Biol 2024; 2790:293-316. [PMID: 38649577 DOI: 10.1007/978-1-0716-3790-6_15] [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] [Indexed: 04/25/2024]
Abstract
Chlorophyll fluorescence imaging provides a noninvasive rapid screen to assess the physiological status of a number of leaves or plants simultaneously. Although there are no standard protocols for chlorophyll fluorescence imaging, here we provide an example of routines for some of the typical measurements.
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Affiliation(s)
- Tracy Lawson
- School of Life Sciences, University of Essex, Colchester, UK.
| | - Silvere Vialet-Chabrand
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, Wageningen, The Netherlands
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13
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Zhang J, Shi H, Yang Y, Zeng C, Jia Z, Ma T, Wu M, Du J, Huang N, Pan G, Li Z, Yuan G. Kernel Bioassay Evaluation of Maize Ear Rot and Genome-Wide Association Analysis for Identifying Genetic Loci Associated with Resistance to Fusarium graminearum Infection. J Fungi (Basel) 2023; 9:1157. [PMID: 38132758 PMCID: PMC10744209 DOI: 10.3390/jof9121157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/23/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Gibberella ear rot (GER) caused by Fusarium graminearum (teleomorph Gibberella zeae) is one of the most destructive diseases in maize, which severely reduces yield and contaminates several potential mycotoxins in the grain. However, few efforts had been devoted to dissecting the genetic basis of maize GER resistance. In the present study, a genome-wide association study (GWAS) was conducted in a maize association panel consisting of 303 diverse inbred lines. The phenotypes of GER severity were evaluated using kernel bioassay across multiple time points in the laboratory. Then, three models, including the fixed and random model circulating probability unification model (FarmCPU), general linear model (GLM), and mixed linear model (MLM), were conducted simultaneously in GWAS to identify single-nucleotide polymorphisms (SNPs) significantly associated with GER resistance. A total of four individual significant association SNPs with the phenotypic variation explained (PVE) ranging from 3.51 to 6.42% were obtained. Interestingly, the peak SNP (PUT-163a-71443302-3341) with the greatest PVE value, was co-localized in all models. Subsequently, 12 putative genes were captured from the peak SNP, and several of these genes were directly or indirectly involved in disease resistance. Overall, these findings contribute to understanding the complex plant-pathogen interactions in maize GER resistance. The regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the kernel bioassay that can be used for evaluating and selecting elite germplasm resources with GER resistance in maize.
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Affiliation(s)
- Jihai Zhang
- Yibin Academy of Agricultural Sciences, Yibin 644600, China
| | - Haoya Shi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Yong Yang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Cheng Zeng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Zheyi Jia
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Tieli Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Mengyang Wu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Juan Du
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Ning Huang
- Yibin Academy of Agricultural Sciences, Yibin 644600, China
| | - Guangtang Pan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Zhilong Li
- Yibin Academy of Agricultural Sciences, Yibin 644600, China
| | - Guangsheng Yuan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Key Laboratory of Biology and Genetic Improvement of Maize in Southwest Region of Ministry of Agriculture, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
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14
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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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15
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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16
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Thakur R, Devi R, Lal MK, Tiwari RK, Sharma S, Kumar R. Morphological, ultrastructural and molecular variations in susceptible and resistant genotypes of chickpea infected with Botrytis grey mould. PeerJ 2023; 11:e15134. [PMID: 37009149 PMCID: PMC10064989 DOI: 10.7717/peerj.15134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
Biotic stress due to fungal infection is detrimental to the growth and development of chickpea. In our study, two chickpea genotypes viz Cicer pinnatifidum (resistant) and PBG5 (susceptible) were inoculated with (1 × 104 spore mL−1) of nectrotrophic fungus Botrytis cinerea at seedling stage. These seedlings were evaluated for morphological, ultrastructural, and molecular differences after 3, 5 and 7 days post inoculation (dpi). Visual symptoms were recorded in terms of water-soaked lesions, rotten pods and twigs with fungal colonies. Light and scanning electron microscopy (SEM) revealed the differences in number of stomata, hyphal network and extent of topographical damage in resistant (C. pinnatifidum) and susceptible (PBG5) genotypes, which were validated by stomatal index studies done by using fluorescence microscopy in the infection process of B. cinerea in leaves of both chickpea genotypes. In case of control (water inoculated) samples, there were differences in PCR analysis done using five primers for screening the genetic variations between two genotypes. The presence of a Botrytis responsive gene (LrWRKY) of size ~300 bp was observed in uninoculated resistant genotype which might have a role in resistance against Botrytis grey mould. The present investigation provides information about the variation in the infection process of B. cinerea in two genotypes which can be further exploited to develop robust and effective strategies to manage grey mould disease.
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Affiliation(s)
- Richa Thakur
- Department of Biochemistry, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Rajni Devi
- Department of Microbiology, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Milan Kumar Lal
- Division of Crop Physiology, Biochemistry and Post harvest Technology, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India
| | - Rahul Kumar Tiwari
- Division of Plant Protection, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India
| | - Sucheta Sharma
- Department of Biochemistry, Punjab Agricultural University, Ludhiana, Punjab, India
| | - Ravinder Kumar
- Division of Plant Protection, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India
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17
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Ginzburg DN, Rhee SY. Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography. Bio Protoc 2023; 13:e4593. [PMID: 36789161 PMCID: PMC9901466 DOI: 10.21769/bioprotoc.4593] [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: 08/24/2022] [Revised: 11/07/2022] [Accepted: 12/25/2022] [Indexed: 01/20/2023] Open
Abstract
Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.
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Affiliation(s)
- Daniel N. Ginzburg
- Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
| | - Seung Y. Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, United States
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18
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Sharma N, Banerjee BP, Hayden M, Kant S. An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse. PLANTS (BASEL, SWITZERLAND) 2023; 12:317. [PMID: 36679030 PMCID: PMC9866171 DOI: 10.3390/plants12020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/03/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.
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Affiliation(s)
- Neelesh Sharma
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
| | - Bikram Pratap Banerjee
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
| | - Matthew Hayden
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, 110 Natimuk Rd, Horsham, VIC 3400, Australia
- AgriBio, Centre for AgriBioscience, Agriculture Victoria, 5 Ring Road, Melbourne, VIC 3083, Australia
- School of Applied Systems Biology, La Trobe University, Melbourne, VIC 3083, Australia
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19
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Patel R, Mitra B, Vinchurkar M, Adami A, Patkar R, Giacomozzi F, Lorenzelli L, Baghini MS. Plant pathogenicity and associated/related detection systems. A review. Talanta 2023; 251:123808. [DOI: 10.1016/j.talanta.2022.123808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 11/24/2022]
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20
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TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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21
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Sapoukhina N, Boureau T, Rousseau D. Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset. FRONTIERS IN PLANT SCIENCE 2022; 13:969205. [PMID: 36438124 PMCID: PMC9685808 DOI: 10.3389/fpls.2022.969205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.
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Affiliation(s)
| | - Tristan Boureau
- Phenotic Platform, Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, Angers, France
| | - David Rousseau
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, Angers, France
- Laboratoire Angevine de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Angers, France
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22
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McDonald SC, Buck J, Li Z. Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot. PLANT METHODS 2022; 18:103. [PMID: 35974392 PMCID: PMC9382788 DOI: 10.1186/s13007-022-00934-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 08/05/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity. RESULTS The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1-5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images. CONCLUSION Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.
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Affiliation(s)
- Samuel C McDonald
- Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA
| | - James Buck
- Department of Plant Pathology, University of Georgia, Griffin, GA, USA
| | - Zenglu Li
- Institute of Plant Breeding, Genetics, and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, USA.
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23
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Egan LM, Stiller WN. The Past, Present, and Future of Host Plant Resistance in Cotton: An Australian Perspective. FRONTIERS IN PLANT SCIENCE 2022; 13:895877. [PMID: 35873986 PMCID: PMC9297922 DOI: 10.3389/fpls.2022.895877] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/06/2022] [Indexed: 05/24/2023]
Abstract
Cotton is a key global fiber crop. However, yield potential is limited by the presence of endemic and introduced pests and diseases. The introduction of host plant resistance (HPR), defined as the purposeful use of resistant crop cultivars to reduce the impact of pests and diseases, has been a key breeding target for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program. The program has seen success in releasing cultivars resistant to Bacterial blight, Verticillium wilt, Fusarium wilt, and Cotton bunchy top. However, emerging biotic threats such as Black root rot and secondary pests, are becoming more frequent in Australian cotton production systems. The uptake of tools and breeding methods, such as genomic selection, high throughput phenomics, gene editing, and landscape genomics, paired with the continued utilization of sources of resistance from Gossypium germplasm, will be critical for the future of cotton breeding. This review celebrates the success of HPR breeding activities in the CSIRO cotton breeding program and maps a pathway for the future in developing resistant cultivars.
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24
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Elliott K, Berry JC, Kim H, Bart RS. A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity. PLANT METHODS 2022; 18:86. [PMID: 35729628 PMCID: PMC9210806 DOI: 10.1186/s13007-022-00906-x] [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: 01/04/2022] [Accepted: 05/16/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. RESULTS In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. CONCLUSIONS Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
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Affiliation(s)
- Kiona Elliott
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA
- Division of Biological and Biomedical Sciences, Washington University in Saint Louis, St. Louis, MO, 63110, USA
| | - Jeffrey C Berry
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA
| | - Hobin Kim
- Army and Navy Academy, Carlsbad, CA, 92008, USA
| | - Rebecca S Bart
- Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA.
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Omayio DO, Ndombi ST. Tool for determining levels and classifying; host plant resistance, tolerance to stress, vigour and pathogen virulence in plants. SCIENTIFIC AFRICAN 2022. [DOI: 10.1016/j.sciaf.2022.e01218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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26
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Liu Z, Liu K, Zhang J, Yan C, Lock TR, Kallenbach RL, Yuan Z. Fractional coverage rather than green chromatic coordinate is a robust indicator to track grassland phenology using smartphone photography. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101544] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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27
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Wu Z, Jiang F, Cao R. Study on a new network for identification of leaf diseases of woody fruit plants. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The rapid and effective identification of leaf diseases of woody fruit plants can help fruit farmers prevent and cure diseases in time to improve fruit quality and minimize economic losses, which is of great significance to fruit planting. In recent years, deep learning has shown its unique advantages in image recognition. This paper proposes a new type of network based on deep learning image recognition method to recognize leaf diseases of woody fruit plants. The network merges the output of the convolutional layer of ResNet101 and VGG19 to improve the feature extraction ability of the entire model. It uses the transfer learning method to partially load the trained network weights, reducing model training parameters and training time. In addition, an attention mechanism is added to improve the efficiency of network information acquisition. Meanwhile, dropout, L2 regularization, and LN are used to prevent over-fitting, accelerate convergence, and improve the network’s generalization ability. The experimental results show that the overall accuracy of woody fruit plant leaf diseases identification based on the model proposed in this paper is 86.41% . Compared with the classic ResNet101, the accuracy is improved by 1.71%, and the model parameters are reduced by 96.63% . Moreover, compared with the classic VGG19 network, the accuracy is improved by 2.08%, and the model parameters are reduced by 96.42% . After data set balancing, the overall identification accuracy of woody fruit plant leaf diseases based on the model proposed in this paper can reach 86.73% .
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Affiliation(s)
- Zhao Wu
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Feng Jiang
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
| | - Rui Cao
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
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Machine Learning for Image Analysis: Leaf Disease Segmentation. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2443:429-449. [PMID: 35037219 DOI: 10.1007/978-1-0716-2067-0_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.
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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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30
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Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning. AGRIENGINEERING 2022. [DOI: 10.3390/agriengineering4010010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions.
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McAtee PA, Nardozza S, Richardson A, Wohlers M, Schaffer RJ. A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues. FRONTIERS IN PLANT SCIENCE 2022; 12:808138. [PMID: 35154203 PMCID: PMC8826216 DOI: 10.3389/fpls.2021.808138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
The ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or fit the colours present within segment of the colour space into a single colour value using a thresholding approach. A major drawback of these methodologies is that, through the process of averaging, they tend to synthetically generate values that may not exist within the context of the original image. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission Internationale de l'Eclairage/International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data. Clustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark colourimetric method development.
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Affiliation(s)
- Peter Andrew McAtee
- The New Zealand Institute for Plant & Food Research (PFR), Auckland, New Zealand
| | - Simona Nardozza
- The New Zealand Institute for Plant & Food Research (PFR), Auckland, New Zealand
| | - Annette Richardson
- The New Zealand Institute for Plant & Food Research (PFR), Kerikeri, New Zealand
| | - Mark Wohlers
- The New Zealand Institute for Plant & Food Research (PFR), Auckland, New Zealand
| | - Robert James Schaffer
- The New Zealand Institute for Plant & Food Research (PFR), Motueka, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
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32
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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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33
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Li M, Frank MH, Migicovsky Z. ColourQuant: A High-Throughput Technique to Extract and Quantify Color Phenotypes from Plant Images. Methods Mol Biol 2022; 2539:77-85. [PMID: 35895198 DOI: 10.1007/978-1-0716-2537-8_9] [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] [Indexed: 06/15/2023]
Abstract
Color patterning contributes to important plant traits that influence ecological interactions, horticultural breeding, and agricultural performance. High-throughput phenotyping of color is valuable for understanding plant biology and selecting for traits related to color during plant breeding. Here we present ColourQuant, an automated high-throughput pipeline that allows users to extract color phenotypes from images. This pipeline includes methods for color phenotyping using mean pixel values, a Gaussian density estimator of CIELAB color, and the analysis of shape-independent color patterning by circular deformation.
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Affiliation(s)
- Mao Li
- Donald Danforth Plant Science Center, St. Louis, MO, USA.
| | - Margaret H Frank
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
| | - Zoë Migicovsky
- Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Truro, NS, Canada
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34
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Zhang Z, Qiao Y, Guo Y, He D. Deep Learning Based Automatic Grape Downy Mildew Detection. FRONTIERS IN PLANT SCIENCE 2022; 13:872107. [PMID: 35755646 PMCID: PMC9227981 DOI: 10.3389/fpls.2022.872107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/27/2022] [Indexed: 05/04/2023]
Abstract
Grape downy mildew (GDM) disease is a common plant leaf disease, and it causes serious damage to grape production, reducing yield and fruit quality. Traditional manual disease detection relies on farm experts and is often time-consuming. Computer vision technologies and artificial intelligence could provide automatic disease detection for real-time controlling the spread of disease on the grapevine in precision viticulture. To achieve the best trade-off between GDM detection accuracy and speed under natural environments, a deep learning based approach named YOLOv5-CA is proposed in this study. Here coordinate attention (CA) mechanism is integrated into YOLOv5, which highlights the downy mildew disease-related visual features to enhance the detection performance. A challenging GDM dataset was acquired in a vineyard under a nature scene (consisting of different illuminations, shadows, and backgrounds) to test the proposed approach. Experimental results show that the proposed YOLOv5-CA achieved a detection precision of 85.59%, a recall of 83.70%, and a mAP@0.5 of 89.55%, which is superior to the popular methods, including Faster R-CNN, YOLOv3, and YOLOv5. Furthermore, our proposed approach with inference occurring at 58.82 frames per second, could be deployed for the real-time disease control requirement. In addition, the proposed YOLOv5-CA based approach could effectively capture leaf disease related visual features resulting in higher GDE detection accuracy. Overall, this study provides a favorable deep learning based approach for the rapid and accurate diagnosis of grape leaf diseases in the field of automatic disease detection.
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Affiliation(s)
- Zhao Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China
- College of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Xianyang, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Xianyang, China
| | - Yongliang Qiao
- Faculty of Engineering, Australian Centre for Field Robotics (ACFR), The University of Sydney, Sydney, NSW, Australia
- *Correspondence: Yongliang Qiao
| | - Yangyang Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Xianyang, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Xianyang, China
| | - Dongjian He
- College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Northwest A&F University, Xianyang, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Northwest A&F University, Xianyang, China
- Dongjian He
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Langstroff A, Heuermann MC, Stahl A, Junker A. Opportunities and limits of controlled-environment plant phenotyping for climate response traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1-16. [PMID: 34302493 PMCID: PMC8741719 DOI: 10.1007/s00122-021-03892-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 06/17/2021] [Indexed: 05/19/2023]
Abstract
Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.
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Affiliation(s)
- Anna Langstroff
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
- Institute for Resistance Research and Stress Tolerance, Federal Research Centre for Cultivated Plants, Julius Kühn-Institut (JKI), Erwin-Baur-Strasse 27, 06484, Quedlinburg, Germany
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany.
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36
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Abbas I, Liu J, Amin M, Tariq A, Tunio MH. Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122643. [PMID: 34961113 PMCID: PMC8707265 DOI: 10.3390/plants10122643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 05/14/2023]
Abstract
Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.
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Affiliation(s)
- Irfan Abbas
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China; (I.A.); (M.H.T.)
| | - Jizhan Liu
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China; (I.A.); (M.H.T.)
- Correspondence:
| | - Muhammad Amin
- Institute of Geo-Information & Earth Observation, PMAS Arid Agriculture University, Rawalpindi 46300, Pakistan;
| | - Aqil Tariq
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China;
| | - Mazhar Hussain Tunio
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China; (I.A.); (M.H.T.)
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37
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Waldamichael FG, Debelee TG, Ayano YM. Coffee disease detection using a robust HSV color‐based segmentation and transfer learning for use on smartphones. INT J INTELL SYST 2021. [DOI: 10.1002/int.22747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
| | - Taye Girma Debelee
- Research and Development Cluster Ethiopian Artificial Intelligence Center Addis Ababa Ethiopia
- Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia
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Li M, Coneva V, Robbins KR, Clark D, Chitwood D, Frank M. Quantitative dissection of color patterning in the foliar ornamental coleus. PLANT PHYSIOLOGY 2021; 187:1310-1324. [PMID: 34618067 PMCID: PMC8566300 DOI: 10.1093/plphys/kiab393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/17/2021] [Indexed: 05/04/2023]
Abstract
Coleus (Coleus scutellarioides) is a popular ornamental plant that exhibits a diverse array of foliar color patterns. New cultivars are currently hand selected by both amateur and experienced plant breeders. In this study, we reimagine breeding for color patterning using a quantitative color analysis framework. Despite impressive advances in high-throughput data collection and processing, complex color patterns remain challenging to extract from image datasets. Using a phenotyping approach called "ColourQuant," we extract and analyze pigmentation patterns from one of the largest coleus breeding populations in the world. Working with this massive dataset, we can analyze quantitative relationships between maternal plants and their progeny, identify features that underlie breeder-selections, and collect and compare public input on trait preferences. This study is one of the most comprehensive explorations into complex color patterning in plant biology and provides insights and tools for exploring the color pallet of the plant kingdom.
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Affiliation(s)
- Mao Li
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
| | - Viktoriya Coneva
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
| | - Kelly R Robbins
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14850, USA
| | - David Clark
- Department of Environmental Horticulture, University of Florida, Gainesville, Florida 32611-0670, USA
| | - Dan Chitwood
- Department of Horticulture, Michigan State University, East Lansing, Michigan 48824, USA
- Department of Computational Mathematics, Michigan State University, East Lansing, Michigan 48824, USA
| | - Margaret Frank
- Donald Danforth Plant Science Center, St Louis, Missouri 63132, USA
- School of Integrative Plant Science, Cornell University, Ithaca, New York 14850, USA
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39
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Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection. REMOTE SENSING 2021. [DOI: 10.3390/rs13193976] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.
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Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat. REMOTE SENSING 2021. [DOI: 10.3390/rs13183612] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.
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Porcar-Castell A, Malenovský Z, Magney T, Van Wittenberghe S, Fernández-Marín B, Maignan F, Zhang Y, Maseyk K, Atherton J, Albert LP, Robson TM, Zhao F, Garcia-Plazaola JI, Ensminger I, Rajewicz PA, Grebe S, Tikkanen M, Kellner JR, Ihalainen JA, Rascher U, Logan B. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. NATURE PLANTS 2021; 7:998-1009. [PMID: 34373605 DOI: 10.1038/s41477-021-00980-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 06/28/2021] [Indexed: 05/27/2023]
Abstract
For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.
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Affiliation(s)
- Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland.
| | - Zbyněk Malenovský
- School of Geography, Planning, and Spatial Sciences, College of Sciences Engineering and Technology, University of Tasmania, Hobart, Tasmania, Australia
| | - Troy Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA, USA
| | - Shari Van Wittenberghe
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
- Laboratory of Earth Observation, University of Valencia, Paterna, Spain
| | - Beatriz Fernández-Marín
- Department of Botany, Ecology and Plant Physiology, University of La Laguna (ULL), Tenerife, Spain
| | - Fabienne Maignan
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Yongguang Zhang
- International Institute for Earth System Sciences, Nanjing University, Nanjing, China
| | - Kadmiel Maseyk
- School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UK
| | - Jon Atherton
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Loren P Albert
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
- Biology Department, West Virginia University, Morgantown, WV, USA
| | - Thomas Matthew Robson
- Organismal and Evolutionary Biology, Viikki Plant Science Centre (ViPS), Faculty of Biological and Environmental Science, University of Helsinki, Helsinki, Finland
| | - Feng Zhao
- School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, China
| | | | - Ingo Ensminger
- Department of Biology, Graduate Programs in Cell & Systems Biology and Ecology & Evolutionary Biology, University of Toronto, Mississauga, Ontario, Canada
| | - Paulina A Rajewicz
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Steffen Grebe
- Molecular Plant Biology, University of Turku, Turku, Finland
| | - Mikko Tikkanen
- Molecular Plant Biology, University of Turku, Turku, Finland
| | - James R Kellner
- Institute at Brown for Environment and Society, Brown University, Providence, RI, USA
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
| | - Janne A Ihalainen
- Nanoscience Center, Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Barry Logan
- Biology Department, Bowdoin College, Brunswick, ME, USA
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Payne WZ, Kurouski D. Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review. PLANT METHODS 2021; 17:78. [PMID: 34266461 PMCID: PMC8281483 DOI: 10.1186/s13007-021-00781-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/11/2021] [Indexed: 05/23/2023]
Abstract
Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.
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Affiliation(s)
- William Z Payne
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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Atefi A, Ge Y, Pitla S, Schnable J. Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives. FRONTIERS IN PLANT SCIENCE 2021; 12:611940. [PMID: 34249028 PMCID: PMC8267384 DOI: 10.3389/fpls.2021.611940] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/14/2021] [Indexed: 05/18/2023]
Abstract
Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.
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Affiliation(s)
- Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - Santosh Pitla
- Department of Biological Systems Engineering, University of Nebraska–Lincoln, Lincoln, NE, United States
| | - James Schnable
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, United States
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Bernal E, Deblais L, Rajashekara G, Francis DM. Bioluminescent Xanthomonas hortorum pv. gardneri as a Tool to Quantify Bacteria in Planta, Screen Germplasm, and Identify Infection Routes on Leaf Surfaces. FRONTIERS IN PLANT SCIENCE 2021; 12:667351. [PMID: 34211486 PMCID: PMC8239390 DOI: 10.3389/fpls.2021.667351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/18/2021] [Indexed: 06/13/2023]
Abstract
Imaging technology can provide insight into biological processes governing plant-pathogen interactions. We created and used a bioluminescent strain of Xanthomonas hortorum pv. gardneri (Xgb) to quantify infection processes in plants using tomato as a model. An X. hortorum pv. gardneri is one of the four Xanthomonas species that causes bacterial spots in tomatoes. We used Xgb to quantify bacterial growth in planta, to assess disease severity in resistant and susceptible tomato lines, and to observe infection routes in leaves. A positive and significant linear correlation r (67) = 0.57, p ≤ 0.0001 was observed between bioluminescence signals emitted by Xgb in planta and bacterial populations determined through dilution plating. Based on bioluminescence imaging, resistant and susceptible tomato lines had significantly different average radiances. In addition, there was a positive and significant correlation r = 0.45, p = 0.024 between X. hortorum pv. gardneri-inoculated tomato lines evaluated by bioluminescence imaging and tomatoes rated in the field using the Horsfall-Barrat Scale. Heritability was calculated to compare the genetic variance for disease severity using bioluminescence imaging and classical field ratings. The genetic variances were 25 and 63% for bioluminescence imaging and field ratings, respectively. The disadvantage of lower heritability attained by bioluminescence imaging may be offset by the ability to complete germplasm evaluation experiments within 30 days rather than 90-120 days in field trials. We further explored X. hortorum pv. gardneri infection routes on leaves using spray and dip inoculation techniques. Patterns of bioluminescence demonstrated that the inoculation technique affected the distribution of bacteria, an observation verified using scanning electron microscopy (SEM). We found significant non-random distributions of X. hortorum pv. gardneri on leaf surfaces with the method of inoculation affecting bacterial distribution on leaf surfaces at 4 h postinoculation (hpi). At 18 hpi, regardless of inoculation method, X. hortorum pv. gardneri localized on leaf edges near hydathodes based on bioluminescence imaging and confirmed by electron microscopy. These findings demonstrated the utility of bioluminescent X. hortorum pv. gardneri to estimate bacterial populations in planta, to select for resistant germplasm, and to detect likely points of infection.
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Affiliation(s)
- Eduardo Bernal
- Department of Horticulture and Crop Science, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, United States
| | - Loïc Deblais
- Food Animal Health Research Program, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, United States
| | - Gireesh Rajashekara
- Food Animal Health Research Program, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, United States
| | - David M. Francis
- Department of Horticulture and Crop Science, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, United States
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Zenda T, Liu S, Dong A, Duan H. Advances in Cereal Crop Genomics for Resilience under Climate Change. Life (Basel) 2021; 11:502. [PMID: 34072447 PMCID: PMC8228855 DOI: 10.3390/life11060502] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura P. Bag 1020, Zimbabwe
| | - Songtao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding 071001, China; (S.L.); (A.D.)
- North China Key Laboratory for Crop Germplasm Resources of the Education Ministry, Hebei Agricultural University, Baoding 071001, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding 071001, China
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Catalano C, Di Guardo M, Distefano G, Caruso M, Nicolosi E, Deng Z, Gentile A, La Malfa SG. Biotechnological Approaches for Genetic Improvement of Lemon ( Citrus limon (L.) Burm. f.) against Mal Secco Disease. PLANTS 2021; 10:plants10051002. [PMID: 34067841 PMCID: PMC8157051 DOI: 10.3390/plants10051002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/12/2021] [Accepted: 05/13/2021] [Indexed: 12/17/2022]
Abstract
Among Citrus species, lemon is one of the most susceptible to mal secco disease, a tracheomycosis caused by the mitosporic fungus Plenodomus tracheiphilus, which induces chlorosis followed by leaf drop and progressive desiccation of twigs and branches. Severe infection can cause the death of the plant. Since no effective control strategies are available to efficiently control the pathogen spread, host tolerance is the most desirable goal in the struggle against mal secco disease. To date, both traditional breeding programs and biotechnological techniques were not efficient in developing novel varieties coupling tolerance to mal secco with optimal fruit quality. Furthermore, the genetic basis of host resistance has not been fully deciphered yet, hampering the set-up of marker-assisted selection (MAS) schemes. This paper provides an overview of the biotechnological approaches adopted so far for the selection of mal secco tolerant lemon varieties and emphasizes the promising contribution of marker-trait association analysis techniques for both unraveling the genetic determinism of the resistance to mal secco and detecting molecular markers that can be readily used for MAS. Such an approach has already proved its efficiency in several crops and could represent a valuable tool to select novel lemon varieties coupling superior fruit quality traits and resistance to mal secco.
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Affiliation(s)
- Chiara Catalano
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
| | - Mario Di Guardo
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
| | - Gaetano Distefano
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
| | - Marco Caruso
- CREA, Research Centre for Olive, Fruit and Citrus Crops, Corso Savoia 190, 95024 Acireale, Italy;
| | - Elisabetta Nicolosi
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
| | - Ziniu Deng
- College of Horticulture and Landscape, Hunan Agricultural University, Changsha 410128, China;
| | - Alessandra Gentile
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
- College of Horticulture and Landscape, Hunan Agricultural University, Changsha 410128, China;
- Correspondence:
| | - Stefano Giovanni La Malfa
- Department of Agriculture, Food and Environment (Di3A), University of Catania, via Valdisavoia 5, 95123 Catania, Italy; (C.C.); (M.D.G.); (G.D.); (E.N.); (S.G.L.M.)
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Current Developments and Challenges in Plant Viral Diagnostics: A Systematic Review. Viruses 2021; 13:v13030412. [PMID: 33807625 PMCID: PMC7999175 DOI: 10.3390/v13030412] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/10/2021] [Accepted: 02/18/2021] [Indexed: 12/24/2022] Open
Abstract
Plant viral diseases are the foremost threat to sustainable agriculture, leading to several billion dollars in losses every year. Many viruses infecting several crops have been described in the literature; however, new infectious viruses are emerging frequently through outbreaks. For the effective treatment and prevention of viral diseases, there is great demand for new techniques that can provide accurate identification on the causative agents. With the advancements in biochemical and molecular biology techniques, several diagnostic methods with improved sensitivity and specificity for the detection of prevalent and/or unknown plant viruses are being continuously developed. Currently, serological and nucleic acid methods are the most widely used for plant viral diagnosis. Nucleic acid-based techniques that amplify target DNA/RNA have been evolved with many variants. However, there is growing interest in developing techniques that can be based in real-time and thus facilitate in-field diagnosis. Next-generation sequencing (NGS)-based innovative methods have shown great potential to detect multiple viruses simultaneously; however, such techniques are in the preliminary stages in plant viral disease diagnostics. This review discusses the recent progress in the use of NGS-based techniques for the detection, diagnosis, and identification of plant viral diseases. New portable devices and technologies that could provide real-time analyses in a relatively short period of time are prime important for in-field diagnostics. Current development and application of such tools and techniques along with their potential limitations in plant virology are likewise discussed in detail.
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48
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Zhang C, Mansfeld BN, Lin YC, Grumet R. Quantitative High-Throughput, Real-Time Bioassay for Plant Pathogen Growth in vivo. FRONTIERS IN PLANT SCIENCE 2021; 12:637190. [PMID: 33643365 PMCID: PMC7902728 DOI: 10.3389/fpls.2021.637190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
Effective assessment of pathogen growth can facilitate screening for disease resistance, mapping of resistance loci, testing efficacy of control measures, or elucidation of fundamental host-pathogen interactions. Current methods are often limited by subjective assessments, inability to detect pathogen growth prior to appearance of symptoms, destructive sampling, or limited capacity for replication and quantitative analysis. In this work we sought to develop a real-time, in vivo, high-throughput assay that would allow for quantification of pathogen growth. To establish such a system, we worked with the broad host-range, highly destructive, soil-borne oomycete pathogen, Phytophthora capsici. We used an isolate expressing red fluorescence protein (RFP) to establish a microtiter plate, real-time assay to quantify pathogen growth in live tissue. The system was successfully used to monitor P. capsici growth in planta on cucumber (Cucumis sativus) fruit and pepper (Capsicum annuum) leaf samples in relation to different levels of host susceptibility. These results demonstrate usefulness of the method in different species and tissue types, allowing for highly replicated, quantitative time-course measurements of pathogen growth in vivo. Analyses of pathogen growth during initial stages of infection preceding symptom development show the importance of very early stages of infection in determining disease outcome, and provide insight into points of inhibition of pathogen growth in different resistance systems.
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Affiliation(s)
- Chunqiu Zhang
- Key Laboratory of Biology and Genetic Improvement of Horticultural Crops, Beijing Key Laboratory of Vegetable Germplasm Improvement, National Engineering Research Center for Vegetables, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Graduate Program in Plant Breeding, Genetics and Biotechnology, Department of Horticulture, Michigan State University, East Lansing, MI, United States
| | - Ben N. Mansfeld
- Graduate Program in Plant Breeding, Genetics and Biotechnology, Department of Horticulture, Michigan State University, East Lansing, MI, United States
| | - Ying-Chen Lin
- Graduate Program in Plant Breeding, Genetics and Biotechnology, Department of Horticulture, Michigan State University, East Lansing, MI, United States
| | - Rebecca Grumet
- Graduate Program in Plant Breeding, Genetics and Biotechnology, Department of Horticulture, Michigan State University, East Lansing, MI, United States
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Abstract
Information from crop disease surveillance programs and outbreak investigations provides real-world data about the drivers of epidemics. In many cases, however, only information on outbreaks is collected and data from surrounding healthy crops are omitted. Use of such data to develop models that can forecast risk/no risk of disease is therefore problematic, as information relating to the no-risk status of healthy crops is missing. This study explored a novel application of anomaly detection techniques to derive models for forecasting risk of crop disease from data composed of outbreaks only. This was done in two steps. In the training phase, the algorithms were used to learn the envelope of weather conditions most associated with historic crop disease outbreaks. In the testing phase, the algorithms were used for hindcasting of historic outbreak events. Five different anomaly detection algorithms were compared according to their accuracy in forecasting outbreaks: robust covariance, one-class k-means, Gaussian mixture model, kernel density estimation, and one-class support vector machine. A case study of potato late blight survey data from across Great Britain was used for proof of concept. The results showed that Gaussian mixture model had the highest forecast accuracy at 97.0%, followed by one-class k-means at 96.9%. There was added value in combining the algorithms in an ensemble to provide a more accurate and robust forecasting tool that can be tailored to produce region-specific alerts. The techniques used here can easily be applied to outbreak data from other crop pathosystems to derive tools for agricultural decision support.
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Affiliation(s)
- Peter Skelsey
- Information and Computational Sciences, James Hutton Institute, Dundee, United Kingdom
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50
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Payne WZ, Kurouski D. Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review. FRONTIERS IN PLANT SCIENCE 2021; 11:616672. [PMID: 33552109 PMCID: PMC7854695 DOI: 10.3389/fpls.2020.616672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/17/2020] [Indexed: 05/11/2023]
Abstract
Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.
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Affiliation(s)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
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