Fruits and Vegetables Disease Detection 🍎🥦 (As an example)

A deep learning project to classify diseases in fruits and vegetables using CNNs and traditional ML models (SVM, KNN, Random Forest).

Note: This project is designed to be adaptable for any CNN-based classification dataset, making it a versatile tool for various image classification tasks.

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Table of Contents

• Project Overview
• Dataset
• Installation
• Usage
• Features
• Results
• Contributing

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Project Overview

This project aims to:

1. Detect Diseases: Classify fruits/vegetables as healthy or diseased (e.g., Apple_Healthy vs. Apple_Rotten).
2. Compare Models: Evaluate CNN performance against traditional ML models (SVM, KNN, Random Forest).

Key Features:

• Data preprocessing and augmentation.
• CNN model training with TensorFlow/Keras.
• Traditional ML pipelines with scikit-learn.
• Model accuracy comparison and visualization.
• Confusion matrix and classification report generation.

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Dataset

The dataset is downloaded from Kaggle:
Fruit and Vegetable Disease Dataset

Structure:

• Images of fruits and vegetables categorized as Healthy or Diseased.
• Split into training, validation, and test sets during preprocessing.

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Installation

Follow these steps to set up the project:

1. Clone the Repository:

   git clone https://github.com/SecurDrgorP/Model_Crafter_Project.git
   cd Fruits-and-Vegetables-Disease-Detection

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Usage

To run the project, follow these steps:

1. Prepare the Dataset:

   • Ensure the dataset is downloaded and placed in the data/raw directory.
   • Run the main script to clean and preprocess the dataset:

     python main.py

2. Train the CNN Model:

   • During training, you will be prompted to choose whether to use the custom checkpoint logic:

     Do you want to use the custom checkpoint logic? (y/n):
     

   • Type y to enable saving the model based on the lowest difference between training and validation accuracy and the lowest validation loss.

3. Evaluate Models:

   • The pipeline will automatically evaluate both CNN and traditional ML models and save the results in the results/ directory.

4. View Results:

   • Check the classification reports, confusion matrix, and model comparison CSV in the results/ directory.

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Features

• Custom Checkpoint Logic: Save the CNN model based on the lowest difference between training and validation accuracy and the lowest validation loss.
• Traditional ML Models: Compare CNN performance with SVM, KNN, and Random Forest.
• Visualization: Generate confusion matrices and classification reports for better insights.
• Data Augmentation: Automatically applies augmentation to improve model generalization.

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Results

• CNN Model: Achieved high accuracy in detecting diseases in fruits and vegetables.
• Traditional ML Models: Performance varies depending on the dataset and preprocessing.
• Comparison: Results are saved in results/f1_comparison.png for easy analysis.