/Crop-Care

Primary LanguageJupyter NotebookMIT LicenseMIT

Crop-Care

This project develops an edge computing solution for real-time crop disease detection and classification within agricultural IoT systems. By processing data locally and using knowledge distillation, we aim to reduce latency, improve decision-making speed in remote areas, and ultimately promote sustainable and efficient farming practices.

Installation

  1. Clone the repository:

    git clone https://github.com/SamarthGarg09/Crop-Care.git
    cd Crop-Care
  2. Create the conda environment and install dependencies:

    conda env create -f environment.yml
    conda activate crop-care
  3. Running the Notebooks

    1. Knowledge Distillation:

      • Run notebooks/student_training.ipynb to train the student model using knowledge distillation.
    2. Disease Detection:

      • Run notebooks/disease_severity_detection.ipynb to train the student model for disease detection.
      • Important: Update the checkpoint path in the notebook to use the newly trained student model.
    3. Model Characteristics:

      • Use notebooks/characterisitcs.ipynb to analyze the model's parameters and inference speed.

Model Framework and Architecture

Model Framework and Architecture

Detection Model

Inference Pipeline

Model Statistics

Results

Prediction Results

Training and Evaluation Curves

Performance Metrics

License

This project is licensed under the MIT License

Contact

Contributors

  • Ansh Rusia (2020IMT-012)
  • Samarth Garg (2020IMT-085)
  • Shubhajeet Pradhan (2020IMT-097)
  • Varun Kumar Tiwari (2020IMT-112)