Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models

Jinfeng Guo, Dong Cui, Jinxing Guo, Umut Hasan (), Fengqi Lv and Zixing Li
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Jinfeng Guo: College of Resources and Environment, Yili Normal University, Yining 835000, China
Dong Cui: College of Resources and Environment, Yili Normal University, Yining 835000, China
Jinxing Guo: College of Ecology and Environment, Baotou Teachers’ College, Baotou 014030, China
Umut Hasan: College of Resources and Environment, Yili Normal University, Yining 835000, China
Fengqi Lv: College of Resources and Environment, Yili Normal University, Yining 835000, China
Zixing Li: College of Resources and Environment, Yili Normal University, Yining 835000, China

Agriculture, 2025, vol. 15, issue 10, 1-23

Abstract: Chlorophyll is an essential pigment for photosynthesis in tea plants, and fluctuations in its content directly impact the growth and developmental processes of tea trees, thereby influencing the final quality of the tea. Therefore, achieving rapid and non-destructive real-time monitoring of leaf chlorophyll content (LCC) is beneficial for precise management in tea plantations. In this study, derivative transformations were first applied to preprocess the tea hyperspectral data, followed by the use of the Stable Competitive Adaptive Reweighted Sampling (SCARS) algorithm for feature variable selection. Finally, multiple individual machine learning models and stacking models were constructed to estimate tea LCC based on hyperspectral data, with a particular emphasis on analyzing how the selection of base models and meta-models affects the predictive performance of the stacking models. The results indicate that derivative processing enhances the sensitivity of hyperspectral data to tea LCC; furthermore, compared with individual machine learning models, the stacking models demonstrate superior predictive accuracy and generalization ability. Among the 17 constructed stacking configurations, when the meta-model is fixed, the predictive performance of the stacking model improves continuously with an increase in the number and accuracy of the base models and with a decrease in the structural similarity among the selected base models. Therefore, when constructing stacking models, the base model combination should comprise various models with minimal structural similarity while ensuring robust predictive performance, and the meta-model should be chosen as a simple linear or nonlinear model.

Keywords: derivative transformations; SCARS; stacking model; LCC (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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