Crop Disease Analysis and Detection using GoogleNet Model

Rice consumption has been a major primary food in India for 70% of its population. Nowadays, many crops along with paddy are being affected by several kinds of diseases due to varied environmental changes. Many farmers are suffering losses, and even food supply chain management is being affected. So, this paper utilized a deep-learning model for disease detection using GoogleNet model architecture. In this work, we considered the disease classifications like brown spot, leaf smut, and bacterial leaf blight. The dataset is collected from Kaggle with over 40k images which consist of different crop- based diseases. The proposed GoogleNet model has achieved higher prediction accuracy of 92.50% when compared with the previous methods.