https://github.com/elfgk/date-fruit-image-processing

Date Fruit Image Processing
https://github.com/elfgk/date-fruit-image-processing

cnn cnn-keras image-classification image-processing jupyter-notebook keras-neural-networks python

Last synced: 4 months ago
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Date Fruit Image Processing

• Host: GitHub
• URL: https://github.com/elfgk/date-fruit-image-processing
• Owner: elfgk
• License: apache-2.0
• Created: 2024-12-22T23:56:03.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-12-29T22:17:29.000Z (6 months ago)
• Last Synced: 2025-01-04T22:52:54.244Z (6 months ago)
• Topics: cnn, cnn-keras, image-classification, image-processing, jupyter-notebook, keras-neural-networks, python
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/elfgkk/hurma-tan-ma-sistemi/edit
• Size: 423 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Date Fruit Image Processing

This project focuses on the processing and analysis of date fruit images. The goal is to develop a machine learning model that can classify and analyze images of date fruits, identifying features such as quality, ripeness, and defects.

## Project Overview

The project includes the following steps:

1. **Data Collection:**

   - Images of date fruits were collected for training and testing the model.

2. **Image Preprocessing:**

   - Preprocessing techniques were applied to prepare the images for analysis. This includes resizing, normalization, and data augmentation.

3. **Model Development:**

   - A machine learning model (Convolutional Neural Network - CNN) was trained to classify the images into different categories (e.g., ripe, unripe, defective).

4. **Model Evaluation:**

   - The model's performance was evaluated using metrics such as accuracy, precision, recall, and F1 score.

5. **Deployment (Optional):**

   - The model can be deployed for real-time predictions or used in various applications related to quality control in the agricultural industry.

## Dataset

The dataset used in this project consists of images of date fruits. The images are labeled into different categories based on the ripeness and quality of the fruit.

- `ripe`: Images of fully ripe date fruits.

- `unripe`: Images of unripe date fruits.

- `defective`: Images of defective or damaged date fruits.

## Libraries Used

- `tensorflow` and `keras`: For building and training the deep learning model.

- `opencv` and `PIL`: For image processing.

- `numpy` and `pandas`: For data manipulation.

- `matplotlib` and `seaborn`: For data visualization.

## Getting Started

To get started with this project, follow these steps:

1. Clone or download the repository:

   ```bash

   git clone https://github.com/elfgk/Date-Fruit-Image-Processing.git

   ```

2. Install the required Python libraries.

3. Open the date_fruit_image_processing.ipynb Jupyter notebook and follow the steps for data preprocessing, model training, and evaluation.

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