Evaluation of Crop Health Status With UAS Multispectral Imagery

Abstract

This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an Unmanned Aircraft System (UAS) and the crop health status was assessed through the Green Area Index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs using machine learning pipelines with several regression algorithms (Multiple Linear Models, Support Vector Machines, Random Forests, and Artificial Neural Networks) along with a feature selection strategy. The Random Forests algorithm was shown to be the best algorithm for GAI prediction with an average relative Root Mean Square Error of 10.86% and a Mean Absolute Error of 0.67. The resulting GAI maps and the regression feature space were classified with Random Forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented.