A computer vision system for soybean diseases recognition using UAVs : preliminary results

Soybean has been the main Brazilian agricultural commodity, contributing substantially to the country’s trade balance. However, foliar diseases have hindered the high yield of soybean production, leading to depreciation of the final product. This paper proposes a computer vision system to track soybean foliar diseases in the field using images captured by the low cost UAV model DJI Phantom 3. The proposed system is based on the segmentation method SLIC to detect plant leaves in the images and on visual attributes to describe the features of foliar physical properties, such as color, gradient, texture and shape. Our methodology evaluated the performance of six classifiers, using images captured at 2 meters high above the plantation. Experimental results showed that color and texture attributes lead to higher classification rates, achieving the precision of 97,80%. Results indicate that our approach can support experts and farmers to monitor diseases in soybean fields.