Disease Detection in Leaves and Crops

Introduction

This project focuses on detecting diseases in leaves and crops using deep learning techniques. It includes a Convolutional Neural Network (CNN) model trained on a dataset containing images of healthy leaves/crops and leaves/crops affected by various diseases. The model can classify a given image of a leaf/crop as either healthy or diseased, aiding farmers in early disease detection and management.

Cloning the Code

To clone the code repository to your local files, use the following command:

git clone https://github.com/TarunSamala/Disease-Detection.git

Requirements

Ensure you have the following dependencies installed:

• Python 3
• TensorFlow
• Keras
• NumPy
• Matplotlib
• scikit-learn
• cv2

You can install the dependencies using the following command:

pip install -r requirements.txt

Directory Structure

• Data Analysis/: Contains Notebook for Visualization and Prediction
• Scripts/: Contains the entire model and python script.
• PlantVillage/ : Contains dataset of the requrired leaves
• output/: Contains the model, However the github upload limit exists , it only contains in the admins directory not on github.

Model Overview

The disease detection model is a Convolutional Neural Network (CNN) designed for classifying tomato leaf images into healthy and diseased categories. Here's a concise summary:

• Architecture: The CNN consists of three convolutional layers followed by max-pooling layers for feature extraction, followed by dense layers for classification.
• Training: Trained on a dataset containing images of healthy tomato leaves and leaves affected by leaf mold using the binary cross-entropy loss and Adam optimizer.
• Evaluation: Evaluated on a separate test set, computing metrics like accuracy, precision, recall, and F1-score. A confusion matrix visualizes classification results.
• Deployment: Deployable for real-world applications in crop disease monitoring, facilitating early detection and management of leaf diseases in tomato plants.

Dataset

• Classes: Healthy and Leaf Mold
• Size: Sufficient samples for effective model training and evaluation
• Preprocessing: Standardized to 128x128 pixels, pixel normalization
• Split: Typical 80% training, 20% testing split
• Source: PlantVillage dataset
• Usage: Training and evaluating disease detection model
• Availability: PlantVillage or similar agricultural repositories

Data Analysis and Visualization

• Accuracy: Measures overall model performance, indicating the proportion of correctly classified samples.
• Confusion Matrix: Provides detailed insights into classification results, helping identify misclassifications and model strengths.