🌿 PlantGuard AI – AI-Powered Plant Disease Detection

Developed by Techno Squad

Author: Dhanush R S

🚀 Overview

PlantGuard AI is a web-based application that leverages Artificial Intelligence to detect plant diseases from leaf images. It helps farmers and home gardeners identify issues early, take preventive measures, and apply effective treatments to minimize crop loss.

🌟 Key Features 🖼 Image Upload Upload leaf images using drag-and-drop or file selection. 🤖 AI Disease Detection Fast and accurate identification using trained deep learning models. 📊 Confidence Scoring Displays prediction confidence for transparency. ⚠️ Severity Levels Indicates disease impact (Low, Medium, High). 📘 Plant Encyclopedia Searchable database with disease details, causes, and treatments. 📱 Responsive Design Works seamlessly on desktop and mobile devices. 🛠 Tech Stack Category Tools & Technologies Frontend React (Vite), Tailwind CSS, Axios, React Router Backend Python, Flask, Flask-CORS AI/ML TensorFlow, Keras, NumPy, Pillow Database JSON (Structured disease data) IDE Kiro IDE Version Control Git & GitHub 📂 Project Structure ai-plant-disease-detection/ ├── backend/ │ ├── app.py # Flask server │ ├── model/ # Trained ML models (.h5 / .keras) │ └── disease_info.json # Disease encyclopedia data │ ├── frontend/ # React (Vite) application │ ├── src/ │ └── public/ │ ├── requirements.txt # Python dependencies └── README.md ⚙️ Setup and Installation 1️⃣ Clone the Repository git clone https://github.com/VectorFlow-vvce/Track1_Techno-Squad.git cd Track1_Techno-Squad 2️⃣ Backend Setup

Create virtual environment

python -m venv venv

Activate it

Linux / Mac:

source venv/bin/activate

Windows:

venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Run server

python app.py 3️⃣ Frontend Setup cd frontend

npm install npm run dev 🔮 Future Enhancements 🌿 Multi-Leaf Detection Detect diseases from multiple leaves in a single image 📶 Offline Mode Lightweight model for low-connectivity areas 👨‍🌾 Community Forum Farmers sharing real-world solutions and insights 📷 Real-Time Camera Detection Live disease detection using mobile camera 🤝 Contribution

Contributions are welcome! Feel free to fork the repo, raise issues, or submit pull requests.