ROBERT-ADDO-ASANTE-DARKO/AI-powered-crop-disease-detection

# Crop Disease and Tomato Freshness Detection This project is a web-based application that utilizes **computer vision** and **natural language processing** to detect crop diseases and assess tomato freshness. The application leverages **YOLOv8** for object detection and **Google's Gemini model** for text generation. ## File Structure The project consists of the following files and directories: ```plaintext models/ # Directory containing the pre-trained YOLOv8 models ├── crop_disease_model.pt # YOLOv8 model for crop disease detection └── tomato_freshness_model.pt # YOLOv8 model for tomato freshness detection app.py # Main application file that defines the Gradio interface and inference functions requirements.txt # List of required packages for installation README.md # This file ``` ## Installation To install the required packages, run the following command: ```bash pip install -r requirements.txt ``` ### Required Packages: - `gradio` - For building the web interface - `numpy` - Numerical operations - `opencv-python` - Image processing library - `Pillow` - Image manipulation - `ultralytics` - YOLOv8 model framework - `google-cloud-generativeai` - Google's generative AI for text generation ## Environment Variables The project requires a Google API key for the text generation component. Set the `GOOGLE_API_KEY` environment variable by running the following command: ```bash export GOOGLE_API_KEY="YOUR_API_KEY" ``` Replace `YOUR_API_KEY` with your actual Google API key from Google Cloud. ## Running the Application To run the application, execute the following command in your terminal: ```bash python app.py ``` This will launch the Gradio interface. You can access the application in your web browser by navigating to: ``` localhost ``` ## Usage 1. **Upload an image** of a crop or tomato to the application. 2. **Select the model type**: - Crop …