Welcome to the GitHub repository for my Plant Disease Detection project, a comprehensive solution for identifying and diagnosing plant diseases using deep learning techniques. This project was developed using Google Colab and PyCharm, leveraging the power of machine learning libraries such as TensorFlow and Keras.
Key Features:
Google Colab and PyCharm Integration: The project seamlessly combines the advantages of both Google Colab and PyCharm, offering flexibility in development environments. Google Colab was utilized for collaborative and cloud-based development, while PyCharm facilitated local development with its powerful IDE features.
Deep Learning Model: The core of the project revolves around a state-of-the-art deep learning model trained to recognize various plant diseases. The model was trained on a diverse dataset, enabling it to accurately identify and classify diseases affecting plants.
Docker Deployment: To simplify the deployment process, Docker containers were employed. This ensures consistency across different environments and allows for easy scaling and distribution of the application.
Web-Based Application: The project extends beyond mere model development by providing a user-friendly web-based application. Users can easily upload images of plant leaves, and the application will provide instant predictions regarding the presence of any diseases.
Usage Guide:
Environment Setup: Follow the provided instructions to set up the development environment using Google Colab and PyCharm.
Model Training: Detailed documentation on how to train the deep learning model using the dataset is included, enabling users to customize and extend the model based on their requirements.
Docker Deployment: Learn how to deploy the developed model using Docker containers. This step-by-step guide ensures a smooth and hassle-free deployment process.
Web Application: Explore the web-based application and its features. Instructions on running the application locally or deploying it on a server are provided.