Monitoring of Rice Plant for Disease Detection using Machine Learning

Abstract

Agriculture is an significant source of income and much of an Indian economy depends on agricultural production. Early detection of plant leaf illnesses is essential to boost crop output and profit..Agricultural specialists diagnose most illnesses through the examination of external symptoms. Farmers, however, have restricted access to professionals. This article proposes a fresh method for diagnosing and classifying rice illnesses.Four diseases were detected and categorized as bacterial blight of rice, rice blast, tungro of rice and false smut.By developing an algorithm different features such as shape, color of the Diseased leaf part were extracted. .Diseases have been Classified using SVM (Support vector machine) and classifier k-Nearest Neighbor (k-NN) after extracting all features. Our suggested solution also provides farmers with Diagnosis of plant disease through a scalable cooperative platform based on the Cloud. This is available via a mobile application allowing customers to send photos from various areas of the leaves that automatically diagnose real-time plant diseases.