🍎 Fruit Image Classification using Computer Vision

Overview

This project presents a Computer Vision-based image classification system designed to recognize and classify fruit images into 10 different categories.

The system applies image preprocessing techniques and machine learning methods to identify fruit types from images and evaluate performance on unseen data.

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Dataset

The project uses the Fruit Classification (10 Classes) dataset available on Kaggle.

Classes

• Apple
• Avocado
• Banana
• Cherry
• Kiwi
• Mango
• Orange
• Pineapple
• Strawberry
• Watermelon

Dataset Source

https://www.kaggle.com/datasets/karimabdulnabi/fruit-classification10-class

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Project Workflow

1. Data Preprocessing

• Image loading
• Image resizing
• Grayscale conversion
• Histogram equalization
• Data normalization
• Train-test split

2. Model Training

The model was trained to learn visual patterns from fruit images and classify them into their corresponding categories.

3. Model Evaluation

Performance was evaluated using:

• Accuracy
• Confusion Matrix
• Classification Report

4. Prediction

The trained model was tested on unseen images to assess its classification capability and generalization performance.

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Technologies Used

• Python
• Jupyter Notebook
• NumPy
• Pandas
• OpenCV
• Matplotlib
• Scikit-Learn

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Project Structure

Fruit-Image-Classification/
│
├── dataset/
├── images/
├── notebooks/
├── models/
├── Fruit_Classification.ipynb
├── Report.pdf
└── README.md

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Learning Outcomes

• Computer Vision fundamentals
• Image preprocessing techniques
• Object recognition systems
• Machine Learning model development
• Model evaluation and performance analysis

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Author

Merna Ayman

Artificial Intelligence Student