Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery

Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li () and Shiwei Wei ()
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Songtao Ban: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Minglu Tian: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Dong Hu: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Mengyuan Xu: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Tao Yuan: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Xiuguo Zheng: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Linyi Li: Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
Shiwei Wei: Shanghai Agrobiological Gene Center, Shanghai 201106, China

Agriculture, 2025, vol. 15, issue 5, 1-24

Abstract: This study combines hyperspectral imaging technology with biochemical parameter analysis to facilitate the disease severity evaluation and early detection of lettuce downy mildew. The results reveal a significant negative correlation between the disease index (DI) and the levels of flavonoids ( r = −0.523) and anthocyanins ( r = −0.746), indicating the role of these secondary metabolites in enhancing plant resistance. Analysis of hyperspectral data identified that spectral regions (410–503 nm, 510–615 nm, and 630–690 nm) and vegetation indices like PRI and ARI2 were highly correlated with DI, flavonoids, and anthocyanins, providing potential spectral indicators for disease assessment and early detection. Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R 2 of 0.857, 0.910, and 0.963, respectively. The classification model using PLS, RF, and CNN successfully detected early physiological changes in lettuce within 24 h post-infection (highest accuracy = 0.764), offering an effective tool for early disease detection. The key spectral parameters in the PLS-DA model, like PRI, also demonstrated strong correlations with DI. These findings provide a scientific basis and practical tools for managing lettuce downy mildew and resistance breeding while laying a foundation for broader applications of hyperspectral imaging in plant pathology.

Keywords: hyperspectral imagery; lettuce; downy mildew (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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