Plant disease severity estimation by computer vision methods

Abstract

The first stage results within the framework of the thesis “Investigation of computer vision methods and algorithms in the field of plant diseases detection” are presented. The analysis of the work related to the automatic assessment of plant disease severity was carried out. It was established that for solving problems in this field, convolution neural networks are promising methods, which are currently superior to classical methods of computer vision in terms of accuracy. To assess the severity degree, classification and segmentation architectures of convolutional neural networks are used. Classification architectures are able to take into account disease visual features at different stages of the disease development, but information about the actual affected area is unavailable. On the other hand, solutions based on segmentation architectures provide actual data on the lesion area, but do not grade severity levels according to disease visual features. Based on the result of the research into the application of convolutional neural networks and options for their use, the goal of this study was determined, which is to develop an automatic system capable of determining the lesion area, as well as to take into account disease visual features and the type of immunological reaction of the plant at different stages of disease progress. It is planned to build a system based on the segmentation architecture of a convolutional neural network, which will produce multi-class image segmentation. Such a network is able to divide image pixels into several classes: background, healthy leaf area, affected leaf area. In turn, the class "affected leaf area" will include several subclasses corresponding to the disease visual features at different stages of disease progress.