陈盛德, 陈一钢, 徐小杰, 等. 基于无人机多光谱遥感的玉米LAI监测研究[J]. 华南农业大学学报, 2024, 45(4): 1-10. doi: 10.7671/j.issn.1001-411X.202310025
    引用本文: 陈盛德, 陈一钢, 徐小杰, 等. 基于无人机多光谱遥感的玉米LAI监测研究[J]. 华南农业大学学报, 2024, 45(4): 1-10. doi: 10.7671/j.issn.1001-411X.202310025
    CHEN Shengde, CHEN Yigang, XU Xiaojie, et al. Monitoring of corn leaf area index based on multispectral remote sensing of UAV[J]. Journal of South China Agricultural University, 2024, 45(4): 1-10. doi: 10.7671/j.issn.1001-411X.202310025
    Citation: CHEN Shengde, CHEN Yigang, XU Xiaojie, et al. Monitoring of corn leaf area index based on multispectral remote sensing of UAV[J]. Journal of South China Agricultural University, 2024, 45(4): 1-10. doi: 10.7671/j.issn.1001-411X.202310025

    基于无人机多光谱遥感的玉米LAI监测研究

    Monitoring of corn leaf area index based on multispectral remote sensing of UAV

    • 摘要:
      目的 探究更高效估测玉米 LAI 的无人机多光谱遥感监测模型,实现对玉米叶面积指数(Leaf area index,LAI)的快速预测估算。
      方法 以全生长周期的玉米植株为研究对象,通过多光谱遥感无人机获取玉米植株影像并实地采集玉米LAI,利用多光谱信息研究植被指数与玉米LAI之间的定量关系,并选择相关的植被指数;分别使用多元线性逐步回归、支持向量机回归算法(Support vector machine regression,SVM)、随机森林回归算法(Random forest regression,RF)和基于鲸鱼算法(Whale optimization algorithm,WOA)优化的随机森林算法(WOA-RF)构建玉米LAI预测模型,通过分析对比,选择最优预测模型。
      结果 筛选出的植被指数NDVI、NDRE、EVI、CIG与LAI呈极显著相关(P<0.01),构建了多元线性回归模型、SVM模型、RF模型和WOA-RF模型的预测模型,R2分别为0.873 2、0.878 0、0.917 7和0.940 8,RMSE分别为0.277 5、0.236 5、0.209 0和0.128 7。
      结论 基于WOA-RF的玉米LAI预测模型的预测精度能够满足玉米生产的需要,对玉米生长期间的种植管理具有指导意义。

       

      Abstract:
      Objective In order to achieve a rapid estimation of the leaf area index (LAI) of maize, this study explores a more efficient monitoring model for maize LAI estimation based multispectral remote sensing of unmanned aerial vehicle (UAV).
      Method This study focused on maize plants throughout their entire growth cycle. Multispectral imagery of maize plants was acquired using UAV, and maize LAI were collected in field. The quantitative relationship between vegetation index and maize LAI was investigated using multispectral information to select relevant vegetation indices. Multiple linear stepwise regression, support vector machine regression (SVM), random forest regression (RF), and a random forest algorithm optimized using whale optimization algorithm (WOA-RF) were used to construct maize LAI prediction models, respectively. The best prediction model was selected on the basis of comparison.
      Result The vegetation indices of NDVI, NDRE, EVI and CIG were highly correlated with LAI (P < 0.01). The models of multiple linear regression, SVM, RF, and WOA-RF were constructed, with R-squared values of 0.873 2, 0.878 0, 0.917 7, and 0.940 8 respectively, and the root mean square error (RMSE) values of 0.277 5, 0.236 5, 0.209 0, and 0.128 7 respectively.
      Conclusion The prediction model of maize LAI based on WOA-RF provides a high level of accuracy, which can meet the requirement for maize production. It can be used to guide planting management of maize during the growth period.

       

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