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UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation

Author

Listed:
  • Lulu Zhang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xiaowen Wang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Huanhuan Zhang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Bo Zhang

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Jin Zhang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xinkang Hu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xintong Du

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Jianrong Cai

    (School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Weidong Jia

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Chundu Wu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China
    Jiangsu Province and Education Ministry Cosponsored Synergistic Innovation Center of Modern Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China)

Abstract

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R 2 ) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Suggested Citation

  • Lulu Zhang & Xiaowen Wang & Huanhuan Zhang & Bo Zhang & Jin Zhang & Xinkang Hu & Xintong Du & Jianrong Cai & Weidong Jia & Chundu Wu, 2024. "UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation," Agriculture, MDPI, vol. 14(11), pages 1-26, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1900-:d:1507478
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    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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