Pepper bell leaf disease detection and classification using optimized convolutional neural network

Agriculture production plays a significant role in the country's economy. Diseases are quite natural and common among plants. Identification of diseases in plants is necessary for averting losses in the yield of agricultural products. Manual monitoring of plants requires expertise, immense effort, and excessive time. Automatic detection will not only help in reducing time and effort but will also help in detecting disease at an early stage, as soon as it will start appearing on plant leaves. Recently, image processing in agriculture has attained a surge of interest by researchers. This study presents a five-layered CNN model for automatic detection of plant disease utilizing leaf images. In order to better train a CNN model, 20,000 augmented images are generated. Experimental results demonstrate that proposed optimized-CNN model can predict pepper bell plant leaf as healthy or bacterial with 99.99% accuracy. Robust results make the proposed optimized-CNN model a preliminary warning tool that can be applied as a disease identification system in a real cultivation environment.