A Deep Computer Vision Approach to Detect Eggplant Diseases

Abstract

Farming inputs are very vital, yet they are not always available to farmers. The goal of this research was to construct an intelligence system utilizing the recognition of eggplant diseases utilizing picture treatment techniques in order to educate farmers about eggplant sickness. The lack of data for both disorders encouraged us to develop a standard dataset for two prominent diseases in the laboratory. Pre-trained Eggplant-disease classification Visual Geometry Group 16 (VGG16) resnet50 and inceptionV3 architectures are used in our work. Further, VGG16 was utilized as the 8th convolution layer feature extractor and these features were used to graduate illnesses. An equivalent or in some cases a greater accuracy was shown in the analysis. There have been proposed possible causes for variations in interclass precision and future direction. Our highest accuracy is achieved by VGG16 the accuracy rate is 99.55%.