Analysis of Plant disease in Power Plant Areas Using Deep Learning Techniques

Abstract

Nowadays the plant disease control is a fundamental practice in agriculture food production. Many plant disease-preventing techniques have assumed critical parts in decreasing the recurrence and force of sicknesses. The exhaust gas from power plant industries can change the sensitivity of plants prompts the incidence of new disease caused due to the rise of atmospheric temperature and CO₂ fixation. The main goal of the task is to actualize new deep learning techniques by combining three different architecture VGG 16, Google Net, and GAN offers to foresee the groundnut plant disease incidence and effectively conveyed on agrifield nearer to power plant zone and non-power plant zone. Furthermore, we inspected an assortment of deep learning applications with plant disease imaging, preprocessing, segmentation, and classification division that are firmly interlaced. The best-prepared model accuracy of groundnut disease of 6 classes were 98% for early leaf spot (BLS), 96% for late leaf spot, 95% for rust disease ), 98% for stem root disease, 96% for Alternaria,93 % for anthracnose and 96 % for healthy leaf .the results shows that new deep learning approach for plant disease investigation of field nearer to power plant zone and non-power plant zone offers a quick, moderate, and effectively deployable procedure for computerized plant disease recognition.