This project aims to develop a system that detects and classifies crop diseases using a deep learning model, specifically YOLOv8. The system is implemented in a Google Colab environment and uses the Roboflow API for dataset management, the Ultralytics library for model training and inference, and Gradio for creating an interactive web interface. This system helps in automating the identification of crop diseases, potentially leading to improved crop management and yield.

Develop a cropdisease detection and classification system using YOLOv8 in Google Colab. Leverage deep learning, Roboflow, and Gradio to create an interactive platform for accurate identification of crop diseases in images and videos.

This project utilizes YOLOv8 to build a crop disease detection and classification system in Google Colab. The system processes images and videos to identify diseases, providing an interactive interface for real-time analysis using Gradio.

Crop Disease Detection Using YOLOv8

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