https://github.com/vignesh-p3007/mangomedix

MangoMediX 🌿 – AI-powered mango leaf disease prediction system using ResNet50, Flask, and a user-friendly web interface. Detects 8 mango leaf diseases with 92% accuracy and provides treatment suggestions.
https://github.com/vignesh-p3007/mangomedix

agriculture-ai cnn computer-vision flask keras mango-disease-detection plant-disease-detection resnet50 smart-farming student-mini-project tensorflow transfer-learning

Last synced: 2 months ago
JSON representation

MangoMediX 🌿 – AI-powered mango leaf disease prediction system using ResNet50, Flask, and a user-friendly web interface. Detects 8 mango leaf diseases with 92% accuracy and provides treatment suggestions.

• Host: GitHub
• URL: https://github.com/vignesh-p3007/mangomedix
• Owner: vignesh-p3007
• Created: 2025-08-24T13:32:37.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-08-24T18:58:27.000Z (11 months ago)
• Last Synced: 2025-08-24T19:28:22.143Z (11 months ago)
• Topics: agriculture-ai, cnn, computer-vision, flask, keras, mango-disease-detection, plant-disease-detection, resnet50, smart-farming, student-mini-project, tensorflow, transfer-learning
• Language: JavaScript
• Homepage:
• Size: 882 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

Awesome Lists containing this project

README

          
# 🌿MangoMediX 

**AI-Powered Mango Leaf Disease Prediction System**  

MangoMediX is a deep learning–based web application that predicts mango leaf diseases from uploaded images.  

With an accuracy of **92%**, it empowers farmers and researchers with **real-time diagnosis** and **actionable treatment recommendations**, reducing reliance on manual inspections.  

---

## ✨ Features

- 📸 Upload mango leaf images via a clean web interface  

- 🤖 Predicts 8 mango leaf conditions:

  - Anthracnose  

  - Bacterial Canker  

  - Cutting Weevil  

  - Die Back  

  - Gall Midge  

  - Healthy  

  - Powdery Mildew  

  - Sooty Mould  

- 📊 Displays prediction confidence score  

- 💡 Provides disease-specific treatment suggestions  

- 🖥️ User-friendly, responsive design  

- 🧠 Model built on **ResNet50 transfer learning** with TensorFlow/Keras  

---

## 🛠️ Tech Stack

- **Frontend:** HTML, CSS, JavaScript  

- **Backend:** Python, Flask, Flask-CORS  

- **Deep Learning:** TensorFlow, Keras, ResNet50 (Transfer Learning)  

- **Storage/Reports:** JSON (training history & evaluation metrics)  

---

## ⚙️ Setup Instructions

1. **Clone the repository**

   - git clone https://github.com/vignesh-p3007/MangoMedix.git

   - cd MangoMedix

2. **Create a virtual environment (recommended)**

     - python -m venv venv

     - source venv/bin/activate   # for Linux/Mac

     - .\venv\Scripts\activate    # for Windows

3. **Install dependencies**

     - pip install -r requirements.txt

4. **Run the web app**

      - python backend/app.py

5. **Access in browser**

      - http://127.0.0.1:5000/

    

---

## Screenshots

Here are some screenshots of the MangoMedix web application:

### 1. Home Page

![Home Page](screenshots/home_page.png)

### 2. Upload Leaf Image

![Upload Page](screenshots/upload_page.png)

### 3. Prediction Result

![Prediction Result](screenshots/prediction_result.png)

---

### Pre-trained Model

The trained model `disease_detector.h5` (~271 MB) is hosted externally due to GitHub file size limits.  

**Download the model here:** [Google Drive Link](https://drive.google.com/file/d/1IR_KaRTu36PF7MKqXWnoakgL6z2iGNpX/view?usp=drive_link)  

**Instructions:**

1. Download the `disease_detector.h5` file from the link above.

2. Place the file in the `backend/` folder of the MangoMedix project.

3. Run the web application as usual:

   

  python backend/app.py

---

## Demo

Watch the MangoMedix web app in action:

**Demo Video:** [Click Here to View](https://drive.google.com/file/d/1vjyj6GXkkCFYufw_3UGDOo0Vt-6c4dT2/view?usp=drive_link)  

---

## 🚀 Live Demo

Check out the live demo of MangoMediX here:  

👉 [Click to View](https://huggingface.co/spaces/vignesh-p3007/MangoMediX)

---

## 📊 Results & Accuracy

- Achieved 92% prediction accuracy using ResNet50 with transfer learning

- Early detection helps prevent disease spread and optimize resource usage

- Scalable to larger datasets and adaptable for other crops

---

## 🚀 Future Improvements

- Expand dataset to include more diseases

- Mobile-friendly version for farmers

- Deploy on cloud (Render, Hugging Face Spaces, or AWS)

- Add notifications for disease alerts

- Multilingual support

---

## 📜 License

This project is licensed under the MIT License.

---

## 👨‍💻 Authors

- [Vignesh](https://www.linkedin.com/in/vignesh-p3007)  

- [Kshitij R Amin](https://www.linkedin.com/in/kshitij-r-amin-9a2274208/)  

- [Nitheesh Ishwar Naik](https://www.linkedin.com/in/nitheeshnaik)  

- [Suhas S](https://www.linkedin.com/in/suhas-s-2773082a6/)  

**Guided by:** Mrs. Usha C S, Assistant Professor, AJIET Mangalore