Flask-TF-API: Corn Leaf Disease Prediction
Description:
This project is a REST API developed using Flask that leverages a TensorFlow deep learning model to predict diseases in corn leaves. The API processes images of corn leaves uploaded by users and returns the predicted disease class along with a confidence score, making it a valuable tool for agricultural diagnostics and precision farming.
Key Features:
- Deep Learning-Powered Predictions: Utilizes a pre-trained TensorFlow model to accurately classify corn leaf diseases.
- RESTful API Architecture: Built with Flask, ensuring scalability, modularity, and ease of integration with other systems.
- Image Processing: Accepts images through API requests, processes them efficiently, and returns precise predictions.
- Confidence Scoring: Provides a detailed confidence percentage for each prediction to enhance decision-making.
- Dockerized Deployment: Includes a Dockerfile to simplify deployment and ensure consistent runtime environments.
Tech Stack:
- Backend Framework: Flask (Python)
- Machine Learning: TensorFlow for deep learning-based classification.
- Containerization: Docker for consistent and scalable deployment.
- Languages:
- Python (91%): Core language for the API and machine learning model integration.
- Dockerfile (9%): For containerization to streamline deployment.
API Workflow:
- Image Upload: Users send an image of a corn leaf via an HTTP POST request.
- Preprocessing: The API preprocesses the image to match the input requirements of the TensorFlow model.
- Prediction: The TensorFlow model predicts the class (e.g., healthy, diseased).
- Result: The API returns the predicted disease class and the corresponding confidence score in JSON format.
Highlights of the Project:
- Advanced Machine Learning Integration: Combines Flask’s lightweight API functionality with TensorFlow’s powerful deep learning capabilities.
- Scalable and Portable: Fully containerized using Docker for consistent performance across environments.
- User-Friendly API Design: Designed with simplicity and usability in mind, making it easy to integrate into larger agricultural systems.
- Real-World Application: Provides a practical solution for farmers and researchers to identify corn leaf diseases quickly and accurately.
Possible Use Cases:
- Precision Agriculture: Helps farmers detect corn diseases early, improving crop health and yield.
- Research Tools: Assists researchers in analyzing plant health and disease patterns.
- Educational Purposes: Can be used to demonstrate the integration of deep learning models into production-ready APIs.
Challenges Overcome:
- Model Integration: Successfully integrated a TensorFlow model into a Flask API while maintaining high inference speed.
- Image Preprocessing: Ensured robust preprocessing to handle a variety of input image formats and qualities.
- Deployment Automation: Streamlined deployment using Docker to eliminate environment-specific issues.
Achievements:
- Successfully developed and deployed a REST API capable of real-time disease prediction.
- Delivered a scalable and efficient solution for integrating machine learning into agricultural diagnostics.
- Gained expertise in combining Flask, TensorFlow, and Docker for production-ready applications.