Crop and Weed Identification in Sugarcane Fields Using RGB Uav Imagery

Abstract

The presence of weeds in the sugarcane crop can affect its production. Usually, farmers use herbicides to control weeds. Precision agriculture is an alternative to avoid damages to the environment, and it is a way to save economic resources. Once, herbicides will be applied only in the appropriate places and correct dosages. Precision agriculture requires georeferenced information on the location of the infestation spots. In this sense, UAVs can fly at low altitudes to play an important role. This permit takes images with the required spatial resolution for a good classification of the weeds present in the crop. The UAV also can fly on demand, solving the problem of temporal resolution. This work proposes using RGB cameras assembled in UAVs to capture images to classify weeds in sugarcane crops. The RGB camera is a solution affordable for a large number of producers. In tests realized in an experimental field with sugarcane, four weeds' species, and soil, using Artificial Neural Networks classifier, was obtained 72.33% to 77.33% of overall accuracy and Kappa coefficient of 0.668 to 0.724.