Above-ground Biomass Wheat Estimation: Deep Learning with UAV-based RGB Images

Abstract

Evaluation of biomass is essential in agriculture to delineate crop management practices, and this is usually done manually, which is time-consuming and destructive. This work proposes an artificial neural network and convolutional neural network to estimate the above-ground biomass (AGB) of wheat using visible spectrum images captured by an unmanned aerial vehicle. The utilized dataset has two Brazilian wheat types, called Parrudo and Toruk. Furthermore, the experimental area has variability in crop growth by varying the amount of nitrogen. To determine AGB, samples of plants were collected at three different crop growth stages, approximately a month apart, making our database spatial and temporal variability. We have shown the feasibility of developing a regression model using RGB images for biomass estimation for two wheat types. The best results for ANN were 489.5, 826.4, and 0.9056 for MAE, RMSE, and (Formula presented.), respectively. In CNN, MAE = 699.2, RMSE = 940.5, and (Formula presented.) = 0.9065. These results show high accuracy in estimation of biomass, and the (Formula presented.) shows good estimation and generalization capacity. The results demonstrate that our methodology can be used in precision agriculture to predict the spatial and temporal variability of AGB.