Time course sensor‐based phenotyping can predict Ascochyta blight disease severity in Cicer species
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Le résumé fourni par la source
Abstract Ascochyta blight is a widely occurring chickpea fungal disease that can cause severe yield loss. Breeding for crop resistance benefits from high‐throughput evaluation of plant–pathogen interactions in genotypes which can serve as sources of resistance. Current practice for the evaluation is human visual scoring of disease symptoms, which is limited in throughput and precision. Here, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons. Pots were imaged at multiple time points with a ground‐based platform, providing 86,792 RGB and 8199 multispectral images. Lesion count was estimated with YOLOv5 (You Only Look Once version 5) object detection (F1 score = 0.27–0.30), fractional green canopy cover was estimated from RGB images, and vegetation indices were extracted from multispectral images. A model trained on growth rates of fractional green canopy cover normalized to control genotypes could predict disease severity classes with an accuracy of 65% –81 % ( 0.43–0.59) on unseen data from three different seasons. The developed methods provide a pathway to predict visual disease severity scores and support the breeding of crops for disease resistance. They may also be used to characterize disease progression, to find underlying resistance mechanisms, and for early disease detection.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.
- Titre Crossref
- Time course sensor‐based phenotyping can predict Ascochyta blight disease severity in <i>Cicer</i> species
- Date Crossref
- 25/08/2026
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Australian Centre for Plant Functional Genomics pays non établi dans la noticeStructure de recherche
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Wine Australia pays non établi dans la noticeOrganisme public
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South Australian Research and Development Institute pays non établi dans la noticeStructure de recherche
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Adelaide University pays non établi dans la noticeUniversité ou école supérieure
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The University of Adelaide pays non établi dans la noticeUniversité ou école supérieure
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School of Agriculture Australian Plant Phenomics Network pays non établi dans la noticeUniversité ou école supérieure
Australian Centre for Plant Functional Genomics, Wine Australia et South Australian Research and Development Institute, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.