🌱 AI Crop Disease Detection & Recommendation

This project leverages deep learning to detect crop diseases from images and generate targeted pesticide recommendations, empowering precision agriculture through automated image analysis and actionable treatment advice.

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📦 Project Structure

ai-crop-disease-detector/
├── data/             # Datasets (train/test images, external CSVs)
├── example/          # Representative healthy and diseased leaf images
├── notebook/         # Jupyter/Colab notebook(s) for pipeline demonstration
├── pesticide_data/   # Pesticide recommendation datasets (CSVs)
├── requirements/     # Python dependency file(s)
├── results/          # Model metrics, plots, output examples
├── samples/          # Example input images and prediction outputs
├── src/              # Source code: preprocessing, training, inference, utils
├── .gitignore
├── LICENSE
├── README.md

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🚀 Features

• Fast and accurate plant disease classification from images
• CSV-driven pesticide recommendation engine
• Modular, well-documented Python codebase
• End-to-end demo notebook with Google Colab support
• Sample inputs/outputs and example images for easy testing

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⚙️ Installation

Install all project dependencies:

pip install -r requirements/requirements.txt

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🚩 Quick Start

• Try the Demo Notebook:
  Launch and run the main pipeline in your browser:
  

• Test the Model:
  Use images from example/ or samples/ to try out predictions and recommendations via the notebook or scripts in src/.

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📊 Results

• See the results/ folder for training curves and test metrics.
• Review samples/sample_output.json for example predictions and recommendations.

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💾 Data

• All required datasets—with descriptions—are in data/ and pesticide_data/.
• See each folder’s README for structure and usage guidelines.

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📜 License

This project is licensed under the MIT License.

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👤 Author

Made by Kamal-Shirupa.
For questions or contributions, feel free to raise an issue or pull request.