Ghimire Amit, Lee Hong Seok, Yoon Youngnam, Kim Yoonha
Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu, 41566, Republic of Korea.
Department of Integrative Biology, Kyungpook National University, Daegu, 41566, Republic of Korea.
Plant Methods. 2025 Aug 12;21(1):112. doi: 10.1186/s13007-025-01428-y.
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.
病害发生率是导致作物产量降低的关键因素。因此，早期识别作物病害对于将病害发生率的影响降至最低并实现作物产量最大化至关重要。因此，本研究旨在使用高光谱成像（HSI）方法结合机器学习（ML）技术来识别大豆黄斑花叶病毒（SYMMV）。大豆在两种不同的环境条件下种植，即环境I和环境II。在环境I中，大豆植株在营养生长的第三个阶段感染SYMMV，而在环境II中，使用感染的种子。进行逆转录聚合酶链反应以区分感染和未感染的植株。从环境可视化图像软件中的感兴趣区域获得的平均光谱值用作数据，而其各自的波长用作ML模型的特征。信息增益方法用于选择与病害识别相关的特征波长。在两种环境中，653nm至682nm的连续波长均显示出更多的信息增益，表明它们在SYMMV分类中具有重要作用。随机森林和k近邻这两种分类模型在早期阶段对感染和未感染的植株进行分类，准确率超过90%。支持向量机在两种环境中对病害进行分类的平均准确率均>95%，在所选模型中表现最佳。逻辑回归模型的准确率较低，在环境I中超过82%，但在环境II中提高到>90%。这些发现表明，高光谱成像结合机器学习是植物病害传统识别方法的最佳替代方案。
