Automated Leaf Disease Detection System with Machine Learning

Abstract

Abstract: This study provides a detailed review of the application of deep learning techniques in plant protection, with a particular emphasis on the detection of crop leaf diseases. Deep learning has received a lot of attention for its success in feature extraction and machine learning, and it has emerged as a major technique in a variety of disciplines such as image and video processing, audio processing, and natural language processing. When applied to the field of plant disease detection, deep learning allows for more objective and efficient extraction of disease traits, boosting research efficiency and technical improvements.Our study seeks to present a synthesis of recent advances in deep learning applied to agricultural leaf disease detection, highlighting current trends and addressing issues in the field. The paper is an invaluable resource for scholars working on plant pest identification. Specifically, our approach uses the Convolutional Neural Network (CNN) algorithm, attaining an outstanding accuracy rate of 97% in disease identification.