Integrating UAV‐based multispectral remote sensing and machine learning for detection and classification of chocolate spot disease in faba bean

Abstract

Chocolate spot (CS), caused by Botrytis fabae, is one of the most destructive fungal diseases affecting faba bean (Vicia faba L.) globally. This study evaluated 33 faba bean cultivars across two locations and over 2 years to assess genetic resistance and the effect of fungicide application on CS progression. The utility of unmanned aerial vehicle–mounted multispectral camera for disease monitoring was examined. Significant variability was observed in cultivar susceptibility, with Bolivia exhibiting the highest level of resistance and Louhi, Sampo, Vire, Merlin, Mistral, and GL Sunrise proving highly susceptible. Fungicide application significantly reduced CS severity and improved yield. Analysis of canopy spectral signatures revealed the near‐infrared and red edge bands, along with enhanced vegetation index (EVI) and soil adjusted vegetation index, as most sensitive to CS infection, and they had a strong negative correlation with CS severity ranging from −0.51 to −0.71. In addition, EVI enabled early disease detection in the field. Support vector machine accurately classified CS severity into four classes (resistant, moderately resistant, moderately susceptible, and susceptible) based on spectral data with higher accuracy after the onset of disease compared to later in the season (accuracy 0.75–0.90). This research underscores the value of integrating resistant germplasm, sound agronomic practices, and spectral monitoring for effectively identification and managing CS disease in faba bean.

Plant phenotyping relevance

UAVマルチスペクトル画像と機械学習により、ソラマメの病害症状・重症度を圃場で検出・分類する手法を評価しており、植物フェノタイピングが中心的である。

Code and data availability

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