Detection of Plant Diseases using Advanced Deep Learning Methods

Abstract

Plant diseases are a major global danger to crop productivity. The labor-intensive and time-consuming nature of traditional disease detection technologies causes delays in diagnosis and treatment. The progression of automated methods for identifying and diagnosis plant diseases has gained popularity with the growth  of deep learning techniques, especially vision for computers and machine learning. This study examines the application Region based Convolutional Neural Networks (R-CNN) and Visual Geometry Group (VGG) to detect plant disease with highest accuracy of 0.9827 . With particular emphasis on the creation of a reliable and effective system for quick recognition and medical treatment. Used Village dataset and it is compared with existing models and achieved higher accuracy. The results of the experiments show the suggested model works to precisely detect different plant diseases Modern deep learning techniques have made a significant contribution to the diagnosis of plant diseases, providing prospective means of reducing crop losses, raising agricultural output, and securing the world's food supply.