Prediction of soybean yellow mottle mosaic virus in soybean using hyperspectral imaging.

Abstract

Disease incidence is a key factor contributing to reduced crop yield. Thus, early identification of crop diseases is crucial for minimizing the effects of disease incidence and maximizing crop yield. Therefore, this study aims to identify soybean yellow mottle mosaic virus (SYMMV) using the hyperspectral imaging (HSI) method combined with the machine learning (ML) technique. The soybeans were cultivated under two different environmental conditions, namely, EN I and EN II. In EN I, soybean plants were infected with SYMMV at the third vegetative growth stage, whereas in EN II, infected seeds were used. A reverse transcription polymerase chain reaction was conducted to distinguish the infected from noninfected plants. Mean spectrum values obtained from regions of interest in the Environmental Visualizing Images software served as data, while their respective wavelengths were used as features for ML models. The information gain method was used for the selection of characteristic wavelengths associated with disease identification. Continuous wavelengths ranging from 653 nm to 682 nm showed more information gain in both environments, indicating their significant role in SYMMV classification. Two classification models, random forest and k-nearest neighbor, classified the infected and noninfected plants at an early stage with over 90% accuracy. The support vector machine classified the disease with an average accuracy of > 95% across both environments, showing the best performance among the selected models. The logistic regression model showed lower accuracy, exceeding 82% in EN I, but improved to > 90% in EN II. These findings suggest that HSI combined with ML is the best alternative to the traditional method of disease identification in plants.

Plant phenotyping relevance

HSIと機械学習により感染植物を非感染植物から識別し、植物病害状態を推定する手法が研究の中心であるため。

Code and data availability

The paper reports hyperspectral imaging of SYMMV-infected soybean with ML classification, but no public dataset, image, or code repository is deposited. The data availability statement only points to the manuscript and supplementary file, with no authors' public URL or deposit identifier; no request-only asset is named

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