Automated Detection and Management of Leaf Diseases in Plants using Image Processing and Machine Learning

Abstract

This paper presents a system designed to detect and monitor diseases found on the leaves of plants through the aid of image processing and machine learning mechanisms. The system can diagnose the problem from the images it receives from leaves and recommend specific treatment. Common users are able to front-end an image of the leaf through a simple web interface that feeds the images into a central database. In response to an image query, the system uses the GLCM method to feed texture characteristics in and to employ K - means clustering in segmenting disease regions. After this, a Support Vector Machine (SVM) polynomial kernel-based classifier is used for the multi-class classification of the diseases of the leaves. It also has features that entails information on the diseases identified and treatment plans for them, improving disease control plans. Experimental assessments show that the developed system is truly capable of analyzing the peculiarity of diseased leaves and categorizing the diseases with high prediction accuracy, pointing to a promising application of the proposed system in agricultural industry and plant pathology science. Some possible enhancements for the future could be the expansion of more functionalities or creating a mobile app aimed at enhancing the program’s usage. The proposed system will not only help in moving the agricultural industry forward by providing a dependable, easy to use, error-free system for early diagnosis of diseases and taking initial steps in the mitigation process but also result in improved quality and quantity of produce.