A Machine Learning-Based Crop Diseases Detection and Management System

Abstract

This work proposes an innovative solution to address crop diseases. The objectives include developing a machine-learning model for rapid disease identification and designing a user-friendly mobile application. The machine learning model, employing convolutional neural networks and transfer learning, is integrated into the mobile app for on-the-go disease diagnosis. Key features include image analysis, disease identification, and real-time treatment recommendations. This work termed CropGuard aims to empower farmers, regardless of technical proficiency, through accessible and efficient crop disease management. This aligns with the broader goal of sustainable agriculture by enabling timely interventions, reducing crop losses, and promoting increased productivity.