Identification of Cotton Leaf Mite Damage Stages Using UAV Multispectral Images and a Stacked Ensemble Method

Shifeng Fan, Qiang He, Yongqin Chen, Xin Xu, Wei Guo, Yanhui Lu, Jie Liu and Hongbo Qiao ()
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Shifeng Fan: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Qiang He: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Yongqin Chen: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Xin Xu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Yanhui Lu: Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Jie Liu: Division of Pest Monitoring and Forecasting, National Agricultural Technology Service Center, Beijing 100125, China
Hongbo Qiao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

Agriculture, 2025, vol. 15, issue 21, 1-25

Abstract: Cotton leaf mites are pests that cause irreparable damage to cotton and pose a severe threat to the cotton yield, and the application of unmanned aerial vehicles (UAVs) to monitor the incidence of cotton leaf mites across a vast region is important for cotton leaf mite prevention. In this work, 52 vegetation indices were calculated based on the original five bands of spliced UAV multispectral images, and six featured indices were screened using Shapley value theory. To classify and identify cotton leaf mite infestation classes, seven machine learning classification models were used: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), K-Nearest Neighbors (KNN), decision tree (DT), and gradient boosting decision tree (GBDT) models. The base model and metamodel used in stacked models were built based on a combination of four models, namely, the XGB, GBDT, KNN, and DT models, which were selected in accordance with the heterogeneity principle. The experimental results showed that the stacked classification models based on the XGB, KNN base model, and DT metamodel were the best performers, outperforming other integrated and single individual models, with an overall accuracy of 85.7% (precision: 93.3%, recall: 72.6%, and F1-score: 78.2% in the macro_avg case; precision: 88.6%, recall: 85.7%, and F1 score: 84.7% in the weighted_avg case). This approach provides support for using UAVs to monitor the cotton leaf mite prevalence over vast regions.

Keywords: cotton spider mites; machine learning; UAV; stacking integration; SHAP (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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