Using multispectral spectrometry and machine learning to estimate leaf area index of spring wheat

Abstract

【Objective】 The leaf area index (LAI) is an important trait of plant canopies but challenging to measure accurately at large scales. We studied the feasibility of using multispectral imaging and machine learning to estimate the LAI of spring wheat. 【Method】 The experiment was conducted in a spring wheat field on the Tumochuan Plain in the Yellow River Basin, Inner Mongolia. Images of the spring wheat at the jointing, booting, and grain-filling stages were acquired using a multispectral camera mounted on a DJI P4M UAV. Selected vegetation indices were subjected to principal component analysis (PCA), and the resulting components were used to estimate LAI. We compared six models: multiple linear regression (MLR), decision tree regression (DTR), backpropagation neural network regression (BPNN), gradient boosting decision tree regression (GBDT), support vector machine regression (SVR), and random forest regression (RFR). LAI was calculated separately for each growth stage using different vegetation indices. 【Result】 LAI was significantly correlated with the normalized difference vegetation index (NDVI), modified simple ratio (MSR), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and normalized difference red edge index (NDRE). It showed a weak correlation with the renormalized difference vegetation index (RDVI) during the heading and grain-filling stages, with their correlation coefficients being 0.23 and 0.21, respectively. The BPNN model was most accurate during the jointing stage, with R2, RMSE, and MAE being 0.822, 0.305, and 0.257, respectively. In contrast, the RFR model performed best during the heading, grain-filling, and entire growth periods, with R2 being 0.613, 0.811 and 0.834, RMSE being 0.189, 0.150 and 0.174, and MAE being 0.126, 0.121 and 0.133, respectively. Additionally, the RFR model constructed using data from all three stages was more accurate than models derived from data at individual growth stages. 【Conclusion】 Multispectral data acquired via UAV, combined with machine learning algorithms, can accurately estimate the LAI of spring wheat at various growth stages. Models constructed using data from multiple growth stages are more accurate than those based on a single stage. The models are most accurate for the booting stage and least for the heading stage. Overall, the RFR model provided the most accurate LAI estimates across the three growth stages.

Keywords

• uav
• multispectral
• spring wheat
• leaf area index
• machine learning method
• vegetation index