Plant Disease Detection Using Deep Learning Algorithms: A Systematic Review

Abstract

Plant diseases may have a considerable influence on crop yield and food security. The impact of plant diseases on agricultural output and food security is debatable. Therefore, for effective treatment and prevention of many diseases, early detection and identification are crucial. Recently, deep learning algorithms have drawn increased interest as a means of developing automated plant disease detection systems. This article provides a review of neural network techniques for image data processing, With an emphasis on crop disease detection. It gives a general summary of the data collection methods, deep learning models and architectures, and different image processing methods applied to the analysis of imaging data. This survey aims to facilitate future research in enhancing the system performance and accuracy of deep learning-based plant disease detection, by exploring its potential capabilities.