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.
-
Clone the repository:
git clone https://github.com/SamarthGarg09/Crop-Care.git cd Crop-Care
-
Create the conda environment and install dependencies:
conda env create -f environment.yml conda activate crop-care
-
Running the Notebooks
-
Knowledge Distillation:
- Run
notebooks/student_training.ipynb
to train the student model using knowledge distillation.
- Run
-
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.
- Run
-
Model Characteristics:
- Use
notebooks/characterisitcs.ipynb
to analyze the model's parameters and inference speed.
- Use
-
This project is licensed under the MIT License
- Email: samarthgarg92001@gmail.com
- Ansh Rusia (2020IMT-012)
- Samarth Garg (2020IMT-085)
- Shubhajeet Pradhan (2020IMT-097)
- Varun Kumar Tiwari (2020IMT-112)