A web-based application to classify potato leaf diseases using a deep learning model (CNN). The application achieves 90% training accuracy and 70% operational efficiency for real-life image classification. The project utilizes Flask for deployment, Google Cloud Storage for model storage, and a real-time camera feed for image capture.
This project aims to automate the classification of potato leaf diseases using deep learning techniques. By leveraging a Convolutional Neural Network (CNN), the system classifies potato leaf images into categories such as Healthy, Early Blight, Late Blight, and other common potato diseases. The model is deployed using the Flask web framework, providing a simple and user-friendly interface for real-time interaction. A camera feed is integrated to capture live images of potato leaves, which are then processed by the trained CNN model to classify them accurately. The model is stored on Google Cloud Storage, ensuring scalability and easy access. With a training accuracy of 90% and an operational efficiency of 70% in real-time image classification, this system provides a valuable tool for farmers to quickly identify diseases in potato crops and take timely corrective actions.
This dataset contains high-resolution images of potato plants exhibiting various diseases, including early blight, late blight, and healthy leaves. It is curated to aid in the development and testing of image recognition models for accurate disease detection and classification, promoting advancements in agricultural diagnostics.
The dataset consists of the directory plant_village, which contains three subdirectories:
early_blight: Images of potato plants with early blight disease.late_blight: Images of potato plants with late blight disease.healthy_plant: Images of healthy potato plants.
The PlantVillage directory contains the following 3 subdirectories:
early_blightlate_blighthealthy_plant
- Real-time Disease Detection: Capture images via a camera feed for immediate disease classification.
- Deep Learning Model: A CNN trained on a curated dataset for potato leaf diseases.
- Cloud Integration: Google Cloud Storage for seamless model hosting.
- Web Interface: Simple and intuitive UI built with Flask.
📦 potato-disease-classification
├── app/
│ ├── static/ # Static files (CSS, JS, images)
│ ├── model/
│ │ └── Gcp.py #model routs for fetching form gcp bucket
│ └── templates/ # HTML templates
├── requirements.txt # Python dependencies
├── app.py # Main Flask app
└── README.md # Project documentation
- Framework: Python, Flask
- Deep Learning Model: Convolutional Neural Networks (CNN)
- Cloud Storage: Google Cloud Bucket
- Frontend: HTML, CSS, JavaScript
- Camera Integration: Capture images of potato leaves directly via the web interface.
- Image Processing: The CNN model classifies the images into disease categories based on the training data.
- Result Display: The classification results are displayed with actionable insights, including the predicted disease and advice for handling it.