Mango Leaf Disease Detection using YOLOv5 – Mobile App

Introducing a brand-new Mango Leaf Disease Detection System that combines a Flutter application and a YOLOv5 model, offering mango growers and enthusiasts an advanced yet easy-to-use solution. This cutting-edge system provides a comprehensive mango leaf disease detection experience by utilizing Python and AI. The YOLOv5 model is trained to detect and localize diseased areas on mango leaves by analyzing photos submitted by users. The Flutter application serves as an intuitive interface, allowing users to easily capture and upload images of their mango leaves for examination. In addition to detecting diseases, the system's AI algorithms provide care recommendations and suggest appropriate treatments based on the identified disease. The technology also enables real-time mango leaf health monitoring, alerting users to any issues that require immediate attention. Our tests show that this integrated system delivers a smooth and efficient way to monitor and maintain healthy mango crops, assisting users in effectively managing disease prevention and care. This research proposes a state-of-the-art Mango Leaf Disease Detection System that combines a Flutter application with YOLOv5, offering a robust and user-friendly solution for mango farmers. The system utilizes AI-powered image analysis and disease localization, with YOLOv5 trained specifically on mango leaf images. The Flutter application simplifies the process for users to submit leaf photos for disease detection. Additionally, the AI algorithms provide real-time alerts, helping users manage disease control and care recommendations for optimal mango crop health.