LUNTIAN: Optimizing Crop Health Utilizing YOLOv8 Object Detection Algorithm for Plant Disease Detection

This study focuses on developing a user-friendly and cost-effective diagnostic system designed to assist agricultural practitioners in monitoring plant health. The system integrates a machine learning model based on YOLOv8 (You Only Look Once version 8) for accurate plant disease classification using image data. Remote sensing techniques are employed to enable early disease detection, utilizing Raspberry Pi 5 equipped with soil moisture, humidity, and temperature sensors, AI Chatbot along with a webcam for image-based plant disease detection. Real-time data is transmitted to a web platform for visualization and analysis. The detection model employs a Convolutional Neural Network (CNN) and YOLOv8 for high-accuracy classification, evaluated using precision, recall, and mean average precision (mAP) to ensure robust performance across multiple plant disease categories. A web-based application was also developed to allow real-time health monitoring, data visualization, and storage of diagnostic results. Additionally, a database of disease symptoms and management practices was established to support informed decision-making and promote sustainable crop management. The YOLOv8 object detection algorithm effectively identified diseases like Mosaic Virus and Powdery Mildew, with improved precision, recall, and mAP scores. The web platform enhanced user engagement, offering real-time monitoring, data storage, and insights for informed decision-making.