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An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model

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  • Xiaofei Yang

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Qiao Li

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Honghui Li

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Hao Zhou

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Jinyan Zhang

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Xueliang Fu

    (College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

Abstract

Chlorophyll content is an important indicator for estimating potato growth. However, there are still some research gaps in the inversion of canopy chlorophyll content using unmanned aerial vehicle (UAV) remote sensing. For example, it faces limitations of the growth cycle, low parameter accuracy, and single feature selection, and there is a lack of efficient and precise systematic research methods. In this study, an improved Adaptive-Forward Feature Selection (AFFS) algorithm was developed by combining remote sensing data and measured data to optimize the input Vegetation Index (VI) variables. Gradient Boosting Machine (GBM) model parameters were optimized using a hybrid strategy improved Elephant Herd Optimization (EHO) algorithm (CDE-EHO) that combines Differential Evolution (DE) and Cauchy Mutation (CM). The CDE-EHO method optimizes the GBM model, achieving maximum accuracy, according to the testing results. The optimal coefficients of determination (R 2 ) values of the prediction set are 0.663, 0.683, and 0.906, respectively, the Root Mean Squared Error (RMSE) values are 2.673, 3.218, and 2.480, respectively, and the Mean Absolute Error (MAE) values are 2.052, 2.732, and 1.928, respectively, during the seedling stage, tuber expansion stage and cross-growth stage. This approach has significantly enhanced the inversion model’s prediction performance as compared to earlier research. The chlorophyll content in the potato canopy has been accurately extracted in this work, offering fresh perspectives and sources for further research in this area.

Suggested Citation

  • Xiaofei Yang & Qiao Li & Honghui Li & Hao Zhou & Jinyan Zhang & Xueliang Fu, 2025. "An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model," Agriculture, MDPI, vol. 15(11), pages 1-32, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:11:p:1181-:d:1668033
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    References listed on IDEAS

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    1. Bingquan Tian & Hailin Yu & Shuailing Zhang & Xiaoli Wang & Lei Yang & Jingqian Li & Wenhao Cui & Zesheng Wang & Liqun Lu & Yubin Lan & Jing Zhao, 2024. "Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition," Agriculture, MDPI, vol. 14(9), pages 1-18, August.
    2. Jakub Stoklosa & Heloise Gibb & David I. Warton, 2014. "Fast forward selection for generalized estimating equations with a large number of predictor variables," Biometrics, The International Biometric Society, vol. 70(1), pages 110-120, March.
    3. Xiaofei Yang & Hao Zhou & Qiao Li & Xueliang Fu & Honghui Li, 2025. "Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing," Agriculture, MDPI, vol. 15(4), pages 1-24, February.
    4. Wenfeng Li & Kun Pan & Wenrong Liu & Weihua Xiao & Shijian Ni & Peng Shi & Xiuyue Chen & Tong Li, 2024. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion," Agriculture, MDPI, vol. 14(8), pages 1-22, August.
    5. Sneha Kumari & V. G. Venkatesh & Felix Ter Chian Tan & S. Vijayakumar Bharathi & M. Ramasubramanian & Yangyan Shi, 2025. "Application of machine learning and artificial intelligence on agriculture supply chain: a comprehensive review and future research directions," Annals of Operations Research, Springer, vol. 348(3), pages 1573-1617, May.
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