Machine learning-enabled UAV hyperspectral identification of tomato spotted wilt virus in tobacco

作者：Mao Chuntang, Yanan Zhao, Leiguang Wang, Ziyi Yang, Weili Kou, Weiheng Xu, Huan Wang, Xiaolong Zhang, Ning Lu, Guangzhi Di · 发表于：Frontiers in Plant Science · 年份：2025 · DOI：10.3389/fpls.2025.1728043 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Smart Agriculture and AI、Spectroscopy and Chemometric Analyses

Problems: Tomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control. Methodology: A UAV-mounted hyperspectral camera (400-1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms-support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting -with the derived feature variable...