Crop Disease Detection Using YOLOv8

In this project, you will build a complete real-time crop disease detection system using YOLOv8, one of the most powerful object detection models available today. The system analyzes images and videos of crops and identifies diseases with high accuracy using modern deep learning techniques.

You will prepare datasets using Roboflow to label and organize your images, train custom models in Google Colab with GPU support, and evaluate model performance using key metrics like precision, recall, and mAP. This hands-on approach ensures you understand both the theory and practical implementation of computer vision systems.

The project covers end-to-end implementation including data augmentation strategies, model fine-tuning for optimal accuracy, inference optimization, and deployment considerations. You'll learn how to handle real-world challenges such as class imbalances, dataset size limitations, and hardware constraints when deploying to edge devices.

By the end of this project, you will have a fully working AI-powered application deployed using Gradio that can help farmers detect plant diseases early and improve crop health monitoring. This hands-on experience prepares you for production-level machine learning projects and real-world AI applications.