Plant Disease Detection System using Deep Learning

This project presents a deep learning-based plant disease classification system that uses transfer learning to identify plant leaf diseases from images. The system is designed for fast and accurate detection and includes a lightweight UI for ease of use.

Project Overview

Plant diseases can significantly reduce crop yield and quality. To help farmers and agriculturists detect diseases early, we built a system that:

• Accepts plant leaf images as input.
• Predicts the type of disease affecting the plant.
• Displays the predicted disease along with symptoms and suggested cure.

This project leverages EfficientNetB4 via Transfer Learning from TensorFlow and is deployed via a Flask web application.

Key Features

• EfficientNetB4-based model trained on the PlantVillage dataset.
• Sample test images provided.
• Visualization of training performance (Accuracy vs Loss).
• JSON-based disease cure and symptoms lookup.
• Web interface for real-time predictions.

---

Model Details

• Model architecture: EfficientNetB4
• Framework: TensorFlow / Keras
• Technique used: Transfer Learning
• Training accuracy: ~98%
• Test accuracy: ~95%

The model was trained using TensorFlow’s tf.keras.applications.EfficientNetB4 and fine-tuned on the PlantVillage dataset.

🔗 Reference for Model Architecture & Training
Transfer Learning with TensorFlow Guide

---

---

📊 Training Results

---

📁 Dataset

We used the PlantVillage dataset, which includes over 50,000 images of healthy and diseased plant leaves from various species such as tomato, apple, grape, etc.

📦 Dataset Link:
🔗 PlantVillage Dataset

🔬 Analysis Code Reference:
🔗 Training/Analysis Code

---

🧪 Sample Testing

You can test the model using images from the sample_test_images/ folder or upload your own leaf images via the web interface.

---

⚙️ Installation

To run the project locally:

# Clone the repo
git clone https://github.com/yuvraj0412s/Plant--Disease-Detection-System.git
cd Plant--Disease-Detection-System

# (Optional) Create a virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the web app
python app.py

---

🤝 Let's Connect & Collaborate!

If you found this project helpful or interesting, feel free to connect or collaborate!