Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images

Detection of symptoms in grape leaves is a very important factor in preventing a serious disease. An epidemic spread in vineyards has huge economic consequences and therefore it is considered a major challenge for viticulture. Automatic detection of vine diseases can play an important role in addressing the issue of diseases management. This study deals with the problem of identifying infected areas of grapevines using Unmanned Aerial Vehicles (UAV) images in the visible domain. In this paper we propose a method based on Convolutional neural network (CNN) and color information to detect symptoms in the vine yards. We studied and compared performances of CNNs using different color spaces, vegetation indices, as well as the combination of both information. The obtained results showed that CNNs with YUV color space combined with ExGR vegetation index, and CNNs with a combination of ExG, Exit, ExGR vegetation indices yield the best results with accuracy more than 95.8%.