Field-Based Spectral and Metabolomic Analysis of Tea Geometrid ( Ectropis grisescens ) Feeding Stress

Xuelun Luo, Wenkai Zhang, Zhenxiong Huang, Yong He, Jin Zhang () and Xiaoli Li ()
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Xuelun Luo: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Wenkai Zhang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Zhenxiong Huang: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, 63 Xiyuangong Road, Fuzhou 350100, China
Yong He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jin Zhang: Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China
Xiaoli Li: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 13, 1-19

Abstract: Tea is one of the most widely consumed non-alcoholic beverages globally, yet its yield and quality are significantly impacted by herbivory from tea geometrids. To accurately detect herbivory stress in tea leaves, this study integrated metabolomics with visible-near-infrared spectroscopy (VIS-NIRS) to explore its in situ capabilities and underlying mechanisms. The results demonstrated that metabolomic data, combined with PCA-based linear dimensionality reduction, could effectively distinguish between tea leaves subjected to herbivory by different densities of tea geometrids. VIS-NIRS successfully identified herbivore-damaged leaves, achieving an optimal average classification accuracy of 0.857. Furthermore, VIS-NIRS was able to differentiate leaves subjected to herbivory on different days. The application of appropriate preprocessing techniques significantly enhanced temporal classification, achieving the highest average classification accuracy of 0.773. By integrating metabolomics and spectral band analysis, the spectral range of 800–2500 nm was found to more accurately identify leaves exposed to herbivory for a prolonged period. Compared to using the full spectrum, the model built within this wavelength range improved classification accuracy by 10%. In conclusion, this study provides a solid theoretical foundation for the in situ, rapid detection of tea geometrid herbivory stress in the field using VIS-NIRS, offering key technical support for future applications.

Keywords: tea geometrids; stress detection; tea canopy environments; VIS-NIRS (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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