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Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models

Author

Listed:
  • 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
    Institute of Resources and Ecology, 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
    Institute of Resources and Ecology, Yili Normal University, Yining 835000, China
    School of Geographic Sciences, East China Normal University, Shanghai 200241, 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)

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.

Suggested Citation

  • Jinfeng Guo & Dong Cui & Jinxing Guo & Umut Hasan & Fengqi Lv & Zixing Li, 2025. "Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models," Agriculture, MDPI, vol. 15(10), pages 1-23, May.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:10:p:1039-:d:1653386
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