Crop Medic: Intelligent Diagnosis and Treatment Guidance for Crop Diseases using Machine Learning

Abstract

The small and marginal farmers across Pakistan still rely on visual disease diagnosis of the crops which is never 100% accurate since most diseases have similar symptoms. This often leads to an excessive number of agricultural losses as the response is significantly delayed. To tackle this, the research aims to introduce a novel idea which integrates both image processing techniques and machine learning algorithm to build an app for the farmers. Five machine learning models namely Random Forest, Support Vector Machine, Ridge Classifier, Decision Tree, and K-Nearest Neighbor were tested exhaustively on four different crops i.e. cotton, rice, wheat and sugarcane. The analysis results showed the highest testing accuracy for the Random Forest Classifier, indicating its stability during real world applications. A mobile application Crop Medic was also developed to deploy the proposed machine learning model for disease detection, that encourages farmers to rapidly diagnose crops and then in turn cut back their pesticide application thereby encouraging sustainable farming and better crop health. This initiative promotes efficient agricultural systems, productivity growth and reduced environmental impact.