Estimating Aboveground Biomass of Maize Based on Multispectral Remote Sensing and Convolutional Neural Network

Abstract

Rapid and accurate estimation of aboveground biomass (AGB) in maize is crucial for evaluating maize growth and precise field management. Current AGB prediction methods mainly use optical vegetation indexes extracted from UAV images, employing linear models or machine learning algorithms. These methods often result in significant loss of raw image information and face saturation effects during later stages of maize growth, severely degrading model accuracy. UAV images and ground data were collected from maize at the nodulation, silking, and milking stages. The correlations between dry and fresh AGB of maize and eight vegetation indexes at different fertility stages were analyzed. Optimal vegetation indexes were used to build a multilayer perceptron (MLP) model, while UAV multispectral images were used to construct a convolutional neural network (CNN) model to estimate dry and fresh AGB, respectively. The results showed that the accuracy of the MLP-based maize AGB dry weight estimation model was decreased sharply with the advancement of maize fertility, and the R2 values of validation set of MLP model for the three growing seasons were 0.65, 0.23, and 0.32, and the RMSE values were 0.27 t/hm2, 2.15 t/hm2, and 5.03 t/hm2, respectively. The CNN model can better overcome the spectral saturation problem with good accuracy and applicability. The R2 values of the three fertility validation sets were improved by 27.69%, 191.30% and 171.88%, and the RMSE was reduced by 22.22%, 38.14% and 45.53%, respectively. The accuracy of the MLP-based maize AGB fresh weight estimation model was similarly low in the late maize growth stage model, with R2 values of 0.27 and 0.37, and RMSE values of 11.57 t/hm2 and 14.98 t/hm2 for the validation sets of the spatula and milk maturity stages, respectively. The R2 values of the validation set for the two fertility stages of the CNN model was improved by 159.26% and 129.73%, and the RMSE was decreased by 26.62% and 54.01%, respectively. The CNN model using original multispectral images as inputs achieved the best estimation results, providing valuable guidance for monitoring research and precise management of maize at different fertility stages.

Keywords:maize, aboveground biomass, multispectral, unmanned aerial remote sensing, convolutional neural network, multiple fertility stage