Hyperspectral differentiation of three grapevine yellows diseases and symptomatically similar stresses.

Abstract

Purpose The study aims to develop a method for detecting and discriminating grapevine yellows (GY) diseases, including Flavescence dorée (FD), Bois noir (BN), and Palatinate grapevine yellows (PGY), using hyperspectral imaging (HSI). Specifically, it seeks to address the challenge of symptom misclassification among visually similar diseases, such as distinguishing between GY diseases and other biotic and abiotic stresses. Methods Hyperspectral images of detached leaves from field with GY diseases, Grapevine Leafroll-associated Virus (V), leafhopper (LH) infestation, iron deficiency (Fe), or magnesium deficiency (Mg) were acquired using a mobile platform in the laboratory or in the field. The images were taken in the spectral range of 400-1,000 nm. Classification models were trained on the mean spectra of the leaves to distinguish between different classes. Results The model achieved F1-scores ranging from 54.2% to 96.8% for white cultivars and exceeded 95% for all six classes for black cultivars. When limited to GY classes and nonsymptomatic leaves, the F1-scores were 89.8%, 78.7%, 63.4%, and 53.4% for nonsymptomatic, FD, PGY, and BN, respectively. Conclusion The study demonstrates the feasibilityof using HSI to detect and discriminate different biotic and abiotic stresses on grapevine leaves, including distinguishing between symptom-similar GY diseases. The developed mobile platform and classification models show promise for largescale monitoring and diagnosis of GY diseases, potentially improving disease management and reducing the risk of symptom misclassification.

Plant phenotyping relevance

ブドウ葉のハイパースペクトル画像から病害・ストレス状態を識別する手法と移動型プラットフォーム、分類モデルを開発・評価しており、植物表現型取得が中心である。

Code and data availability

The paper's hyperspectral leaf images (4,500 images) and analysis are paper-specific phenotyping assets, but the data availability statement only promises release upon request; no public repository, code deposit, or authors' URL is provided. The supplementary material only contains subsampling tables, not datasets or代码

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