外文摘要：In the cultivation of maize, the leaf area index (LAI) serves as an important metric to determine the development of the plant. Unmanned aerial vehicles (UAVs) that capture RGB images, along with random forest regression (RFR), can be used to indirectly measure LAI through vegetative indices. Research using these techniques is at an early stage, especially in the context of maize for silage. Therefore, this study aimed to evaluate which vegetative indices have the strongest correlations with maize LAI and to compare two regression methods. RFR, ridge regression (RR), support vector machine (SVM), and multiple linear regression (MLR) regressions were performed in Python for comparison using images obtained in an area cultivated with maize for silage. The results showed that the RGB spectral indices showed saturation when the LAI reached 3 m(2) m(-2), with the VEG (vegetable index), COM (combination), ExGR (red-green excess), and TGI (triangular greenness index) indices selected for modeling. In terms of regression, RFR showed superior performance with an R-2 value of 0.981 and a root mean square error (RMSE) of 0.138 m(2) m(-2). Therefore, it can be concluded that RFR using RGB indices is a good way to indirectly obtain the LAI.
外文关键词：remote sensing;Zea mays;Multiple linear regression;remote piloted aircraft;visible spectrum;saturation
作者：de Magalhaes, Leonardo Pinto;Rossi, Fabricio
作者单位：Luiz de Queiroz Coll Agr ESALQ USP;Fac Anim Sci & Food Engn FZEA USP
期刊名称：AGRONOMY-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14(4)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台