Smartagb: Aboveground Biomass Estimation of Sorghum Based on Spatial Resolution, Machine Learning and Vegetation Index

Abstract

This work aims to explore the feasibility of predicting and estimating the aboveground biomass (AGB) of sorghum using multispectral images captured by UAVs, and clarify the quantitative relationship between vegetation index and sorghum AGB based on different spatial resolutions, and build an AGB estimation model based on UAV multispectral images and vegetation index under different spatial resolutions. Combining spatial resolution, vegetation index, and machine learning, a training set is used to train the model, and a verification set is used to verify the model to select the best prediction model corresponding to different spatial resolutions. The three best prediction models under three spatial resolutions are classic machine learning models. 1) when the spatial resolution is 0.017m, the model precision obtained from the random forest is R2=0.8961, MAE=26.4340, and RMSE=32.2459. 2) when the spatial resolution is 0.024m, the model accuracy obtained by the Lasso algorithm is R2=0.8826, MAE=31.106, and RMSE=40.2937; 3) when the spatial resolution is 0.030m, the model accuracy obtained by the decision tree algorithm is R2=0.8568, MAE=30.3373, and RMSE=40.8082; and 4) the model's accuracy decreases with the decrease of spatial resolution. The results show that the combination of spatial resolution, vegetation index, and machine learning algorithm is an effective, fast, and accurate prediction method.