🌿 Leaf Disease Detection using CNN

This project is a deep learning model built using TensorFlow and Keras to detect and classify different types of leaf diseases. It helps in early detection of crop issues by analyzing leaf images and identifying whether the leaf is healthy or affected by a disease.

---

🧠 Project Highlights

• Built with Convolutional Neural Networks (CNN)
• Uses ImageDataGenerator for data augmentation and preprocessing
• Trained on a custom dataset of plant leaf images
• Classifies leaves as:
  • healthy
  • diseased_type1
  • diseased_type2
• Real-time prediction using OpenCV and Matplotlib

---

🗂 Dataset

• The dataset should be organized like this:

dataset/ └── train/ ├── healthy/ ├── diseased_type1/ └── diseased_type2/

• You can use any plant leaf dataset by organizing it into class-labeled subdirectories.
• Images are resized to 128x128 pixels during training.

---

⚙️ Installation

1. Clone this repo

git clone https://github.com/Dhanraj200547/leaf-disease-detection.git cd leaf-disease-detection

🏗️ Model Architecture The CNN model is built using Keras.Sequential API:

Input layer (128x128x3)

Conv2D → MaxPooling

Conv2D → MaxPooling

Flatten → Dense → Softmax

The model is trained for 10 epochs and saved as leaf_disease_model.h5.

python predict.py # to predict the new test image