https://github.com/dheeraj8601/deepleaf-cnn-based-precision-agriculture-for-automated-potato-leaf-disease-classification

DeepLeaf: CNN-based potato disease classification. Utilizes TensorFlow, Keras, and FastAPI. Frontend designed with HTML/CSS/JS. Android app (PotatoPathoGuard) developed in Android Studio. Enables precision agriculture through on-device disease detection.
https://github.com/dheeraj8601/deepleaf-cnn-based-precision-agriculture-for-automated-potato-leaf-disease-classification

android-studio css html java javascript jup pycharm-ide python

Last synced: 3 days ago
JSON representation

DeepLeaf: CNN-based potato disease classification. Utilizes TensorFlow, Keras, and FastAPI. Frontend designed with HTML/CSS/JS. Android app (PotatoPathoGuard) developed in Android Studio. Enables precision agriculture through on-device disease detection.

• Host: GitHub
• URL: https://github.com/dheeraj8601/deepleaf-cnn-based-precision-agriculture-for-automated-potato-leaf-disease-classification
• Owner: Dheeraj8601
• Created: 2023-11-14T07:36:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-17T08:28:23.000Z (about 1 year ago)
• Last Synced: 2024-10-31T17:44:47.989Z (4 months ago)
• Topics: android-studio, css, html, java, javascript, jup, pycharm-ide, python
• Language: Python
• Homepage:
• Size: 110 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# DeepLeaf: CNN-Based Precision Agriculture for Automated Potato Leaf Disease Classification

## 1. training.ipynb (Jupyter Notebook)

- **Technologies Used:** TensorFlow, Keras

- **IDE Used:** Jupyter Notebook

- Utilizes TensorFlow and Keras for CNN-based image classification.

- Dataset: PlantVillage.

- Data preprocessing, augmentation, and model training.

- Evaluation and visualization of training/validation metrics.

- Model saving and conversion to TensorFlow Lite format.

## 2. PlantVillage Dataset

- PlantVillage dataset used for training the CNN model.

Note: Detailed code and data storage specifics are available in the Jupyter Notebook (training.ipynb).

## 3. Front End: Potato Disease Classification

### index.html:

- Main webpage for Potato Disease Classification.

- User-friendly interface for image upload and classification results.

- Responsive design with style customization.

### style.css:

- **Technologies Used:** HTML, CSS

- CSS styling for the front end, enhancing visual appeal and responsiveness.

- Defines layout, colors, and animations for a seamless user experience.

### script.js:

- **Technologies Used:** JavaScript

- JavaScript file handling user interactions.

- Enables image upload, displays a preview, and triggers API calls for disease classification.

## 4. Additional HTML Pages: Disease Information and Contact

## 5. FastAPI Backend and Android App Integration:

### main.py (FastAPI Backend):

- **Technologies Used:** FastAPI, TensorFlow

- **IDE Used:** PyCharm

- Utilizes FastAPI to create an API for potato disease classification.

- Allows CORS for specified frontend URLs.

- Loads a pre-trained TensorFlow model for disease classification.

- Exposes an endpoint "/ping" for a basic health check.

- Exposes an endpoint "/predict" to receive images for classification.

- Returns the predicted class and confidence.

### Android App (PotatoPathoGuard in Android Studio):

- **Technologies Used:** Android Studio, TensorFlow Lite

- Developed using Android Studio.

- Utilizes a TensorFlow Lite (tflite) model produced in the training.ipynb notebook.

- Incorporates functionality to capture or select an image.

- Sends the image to the FastAPI backend "/predict" endpoint for classification.

- Displays the predicted class obtained from the backend.

- Provides a user-friendly interface for interacting with the potato disease classification system.