Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

TLDR

Leaf area index (LAI) is a key indicator of plant growth and yield that can be monitored by remote sensing. The study aimed to identify the optimal regression model for soybean LAI estimation across the entire crop growth period and during individual growth stages using UAV hyperspectral data. Random forest, artificial neural network, and support vector machine regressions were compared against a partial least‑squares model using datasets from SRS and STR sampling. Random forest achieved the highest precision and stability over the whole growth period, while artificial neural network performed best for a single growth phase; both models benefited from STR sampling, with RF suited to large plots and ANN to smaller, more homogeneous plots.

Abstract

Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).