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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.

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Dheeraj8601/DeepLeaf-CNN-Based-Precision-Agriculture-for-Automated-Potato-Leaf-Disease-Classification

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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.

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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.

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