Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil

This research aims to evaluate the performance of machine learning algorithms and multispectral aerial images in estimating wheat grain yield, contributing to the eradication of hunger and food security. Two sampling sites with different cultivation periods were used in this study, with multiple aerial flights conducted throughout the phenological cycle. At the end of the experiment, grain yield (t/ha) was determined. The tested supervised machine learning algorithms included Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), combined with vegetation indices from the visible spectrum (RGB), multispectral indices, and bands. The Linear Regression algorithm, combined with the RGB indices, showed the best performance in the initial and final phases of the crop, with coefficients of determination (R2) of 0.61 and 0.58, respectively. The most robust performance was observed during the booting-heading phase, where the SVM algorithm, combined with the red band, red edge and green band, achieved an R2 of 0.78 and a root mean squared error (RMSE) of 0.479. t/ha. Therefore, the application of these variables and algorithms to estimate wheat productivity proves to be a viable approach, offering an efficient method to predict grain productivity, especially in the southern region of Brazil.