Automated Pest Disease Detection For Agriculture

🌱 Automated Pest & Disease Detection for Agriculture ------------------------------------------------------------------------------------------------------------------------------------------------------ This project is a Machine Learning–based web application designed to automatically detect pests and plant diseases from crop images, helping farmers and agricultural experts take early preventive action and improve crop yield. The system uses a trained deep learning model (TensorFlow/Keras) to analyze uploaded plant images and predict the presence of pests or diseases through a simple Flask-based web interface. 🚀 Project Overview ------------------------------------------------------------------------------------------------------------------------------------------------------------ Agriculture faces significant losses due to late identification of pests and plant diseases. This project addresses that challenge by providing an automated image-based detection system that: Accepts plant images from users Processes images using a trained CNN model Predicts pest or disease presence Displays results through a user-friendly web interface 🛠️ Technologies Used --------------------------------------------------------------------------------------------------------------------------------------------- Python Flask – Web framework TensorFlow / Keras – Deep learning model NumPy – Numerical computations Pillow (PIL) – Image processing HTML/CSS – Frontend interface ------------------------------------------------------------------------------------------------------------------------------------------- 📁 Project Structure project/ │ ├── app.py # Flask application (main server file) ├── model.h5 # Trained deep learning model ├── train_model.py # Model training script ├── data_setup.py # Dataset preprocessing utilities ├── requirements.txt # Project dependencies ├── README.md # Project documentation ├── RUN_GUIDE.md # Instructions to run the project │ ├── dataset/ # Training and testing image dataset │ ├── templates/ │ └── index.html # Web interface (image upload & results) │ ├── static/ # Static files (CSS, images if any) └── Test/ # Test samples ⚙️ How It Works ------------------------------------------------------------------------------------------------------------------------------------------------------------------- User uploads a plant image through the web interface Image is preprocessed using Pillow and NumPy The trained TensorFlow model (model.h5) analyzes the image The system predicts whether the plant is healthy or affected Results are displayed on the web page 🎯 Key Features -------------------------------------------------------------------------------------------------------------------------------------------------------------- Automated pest and disease detection Deep learning–based image classification Simple and interactive web interface Easy to extend with more crop categories Suitable for real-world agricultural use 🌾 Future Enhancements ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Add more crop and disease classes Improve model accuracy with larger datasets Provide treatment and prevention suggestions Deploy on cloud platforms for real-time usage Mobile application Integration ------------------------------- My Requriements --------------------------------- 1. I require a camera option within the project for live detection of plant diseases. 2. The current output is quite small; I necessitate a multi-window output. 3. The output will be enhanced and presented in detail.