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| title: Plant Disease Detector | |
| emoji: π | |
| colorFrom: green | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.34.2 | |
| app_file: app.py | |
| pinned: false | |
| # π Plant Disease Detector | |
| This is a Gradio-based web app for detecting fruit and leaf diseases using an EfficientNetB0-based CNN model. | |
| π§ **Model Overview** | |
| - **Architecture:** EfficientNetB0 (transfer learning) | |
| - **Input Size:** 160Γ160 RGB | |
| - **Classes:** 21 fruit/leaf disease types (mango, guava, lime, pomegranate) | |
| - **Framework:** TensorFlow / Keras | |
| - **Performance:** ~99.5% train accuracy, ~98.5% validation accuracy | |
| πΌοΈ **How to Use** | |
| - Upload a fruit/leaf image via the interface | |
| - The model will return the predicted disease name and confidence % | |
| - You can also try it using the provided example images | |
| π **Example Images** | |
| Sample images are available in the `examples/` folder: | |
| - `Phytopthora.jpg` | |
| - `RedRust.jpg` | |
| - `HealthyMangoLeaf.jpg` | |
| - `LimeLeafSpotted.jpg` | |
| π οΈ **Dependencies** | |
| All dependencies are listed in `requirements.txt`: | |
| - `tensorflow` | |
| - `gradio` | |
| - `numpy` | |
| - `pillow` | |
| πββοΈ **Author** | |
| Aarzoo Singh | |
| Research Intern, Machine Learning | |
| B.Tech CSE, NIT Patna | |
| π [Hugging Face Profile](https://huggingface.co/Aarzoo-Singh2206) | |
| --- | |
| π **Live App:** [Click here to run this app](https://huggingface.co/spaces/Aarzoo-Singh2206/codeblock) |