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This repository contains code for a crop disease prediction and remedies recommendation system. The crop disease prediction and remediation system plays a crucial role in modern agriculture by providing farmers with proactive tools to manage and safeguard their crops effectively. By accurately predicting the likelihood of crop diseases, the system empowers farmers to take preemptive measures, thereby reducing the risk of yield loss and economic damage. Additionally, the recommendation of appropriate remedies, whether pesticide-based or natural, enables farmers to make informed decisions that balance effectiveness with environmental sustainability. Ultimately, this system not only helps optimize crop yields but also contributes to the sustainable and responsible management of agricultural resources, ensuring food security for communities worldwide.

This project serves as the capstone project for the Data Science Bootcamp by neuefische. Team members are: FreyaStein, forster-j, olugbengaoloyade and Ankit-kumar91.

The training dataset Plant Diseases Training Dataset can be found on Kaggle. This dataset contains a collection of images of various crop leaves affected by different diseases. It contains 95868 images and 38 disease types.

Total Images: 95868

Classes: 38 (e.g., 'Apple___Apple_scab', 'Apple___Black_rot', 'Grape___Black_rot', etc.)

• functions: Contains functions for plotting and preprocessing the data
• images: Images needed for the app
• models: Trained model needed for the app
• notebooks: Notebooks the explain loading, EDA, feature engineering and training.
• util.py: Contains helper scripts for the streamlit app.

Clone this repository. Set up a Python environment and install the necessary dependencies listed in requirements.txt. Utilize the provided scripts in the src directory for model training, data preprocessing, etc.

• For installing the virtual environment you can either use the Makefile and run make setup or install it manually with the following commands:

  make setup

  After that active your environment by following commands:

  source .venv/bin/activate

Or ....

• Install the virtual environment and the required packages by following commands:

  pyenv local 3.11.3
  python -m venv .venv
  source .venv/bin/activate
  pip install --upgrade pip
  pip install -r requirements.txt

• Install the virtual environment and the required packages by following commands.

  For PowerShell CLI :

  pyenv local 3.11.3
  python -m venv .venv
  .venv\Scripts\Activate.ps1
  pip install --upgrade pip
  pip install -r requirements.txt

  For Git-bash CLI :

  pyenv local 3.11.3
  python -m venv .venv
  source .venv/Scripts/activate
  pip install --upgrade pip
  pip install -r requirements.txt

  Note: If you encounter an error when trying to run pip install --upgrade pip, try using the following command:

  python.exe -m pip install --upgrade pip

Run the app with the following command: streamlit run main.py