Overview
This project is a machine learning-based web application designed to detect and classify plants as crops or weeds. The model is built using TensorFlow.js and MobileNet, allowing users to upload images and receive predictions in real time.
🚀 Features
Real-time plant classification using MobileNet.
User-friendly UI for uploading and analyzing images.
Visual feedback indicating whether the uploaded image is a crop or a weed.
Deployed at: Crop & Weed Detector
🛠️ Tech Stack
Frontend: React 18, HTML, CSS
Machine Learning: TensorFlow.js, MobileNet Model
Deployment: Val.Town
📥 Installation & Setup
To run the project locally, follow these steps:
Clone the repository:
git clone https://github.com/your-username/crop-weed-detector.git cd crop-weed-detector
Install dependencies:
npm install
Start the development server:
npm run dev
Open the application in your browser at http://localhost:3000
📸 How to Use
Click on the Upload Image button.
Select an image of a crop or weed.
The model will analyze the image and display a prediction with confidence score.
The background color will indicate whether the plant is a crop (green) or weed (red).
🔥 Future Improvements
Train a custom model for higher accuracy on agriculture datasets.
Add batch image processing for multiple uploads.
Optimize performance for mobile and low-end devices.
Enhance UI with better loading indicators and result visualization.
📝 License
This project is open-source and available under the MIT License.
💡 Contributors
Shubhi Mishra
Feel free to contribute by submitting pull requests!