https://github.com/rydzze/ichigo-fruit-classifier
Project for Computer Vision
https://github.com/rydzze/ichigo-fruit-classifier
computer-vision deep-learning fruit-classification python tensorflow
Last synced: about 2 months ago
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Project for Computer Vision
- Host: GitHub
- URL: https://github.com/rydzze/ichigo-fruit-classifier
- Owner: rydzze
- Created: 2024-12-27T06:46:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-13T01:56:49.000Z (over 1 year ago)
- Last Synced: 2025-02-13T02:35:40.400Z (over 1 year ago)
- Topics: computer-vision, deep-learning, fruit-classification, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 1.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# 🍓 Ichigo: Intelligent Multi-Fruit Classification and Quality Analysis System
## 📌 Introduction
Traditional fruit classification methods are **labor-intensive and error-prone**. **Ichigo** is an **AI-driven system** that utilizes deep learning models like **CNN, ResNet-50, and VGG-16** to classify fruits as **fresh or rotten**. By integrating machine learning with advanced image processing, **Ichigo** enhances food quality control, reduces waste, and optimizes supply chains. Designed to be **scalable and accessible**, it serves **small farms and large food industries** alike.
## ❗ Problem Statements
Despite advances in **computer vision and AI**, challenges persist in fruit classification:
🔸 **Manual inspection is prone to inconsistencies** – Human judgment can be subjective, leading to quality control issues.
🔸 **Limited datasets hinder classification accuracy** – AI models struggle with underrepresented fruit types.
🔸 **Scalability for real-time processing** – Many systems fail to handle large-scale, high-speed operations efficiently.
## 🎯 Objectives
The **Ichigo** system aims to:
✅ **Enhance classification accuracy** using deep learning.
✅ **Enable real-time quality assessment** for efficient sorting.
✅ **Ensure scalability** for farms, supermarkets, and industries.
## 🔥 System Features
🚀 **Deep Learning-Based Classification** – Uses **CNN, ResNet-50, and VGG-16**.
🖥️ **Web-Based Interface** – Built with **Flask, HTML, CSS, and JavaScript**.
📊 **Data Preprocessing Techniques** – Image **enhancement, sharpening, and edge detection**.
🔍 **Real-Time Object Detection** – Segmentation and bounding box.
## 📊 Model Performance
### 📌 Public Dataset Performance (Without Preprocessing)
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---------|-------------|-------------|------------|-------------|
| **CNN** | 72.50 | 74.50 | 72.75 | 70.84 |
| **ResNet50** | **98.75** | **98.76** | **98.71** | **98.72** |
| **VGG16** | 97.03 | 97.08 | 96.92 | 96.96 |
### 📌 Public Dataset Performance (With Preprocessing)
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---------|-------------|-------------|------------|-------------|
| **CNN** | 72.81 | 73.01 | 72.70 | 71.88 |
| **ResNet50** | **99.22** | **99.21** | **99.20** | **99.20** |
| **VGG16** | 97.81 | 97.69 | 97.68 | 97.65 |
#### 🔗 Public Dataset Used
> Sultana, Nusrat; Jahan, Musfika; Uddin, Mohammad Shorif (2022), “Fresh and Rotten Fruits Dataset for Machine-Based Evaluation of Fruit Quality”, Mendeley Data, V1, doi: [10.17632/bdd69gyhv8.1](https://doi.org/10.17632/bdd69gyhv8.1)
### 📌 Self-Collected Dataset Performance (Without Preprocessing)
| Model | Accuracy 1 (%) | Accuracy 2 (%) | Average (%) |
|---------|--------------|--------------|------------|
| **CNN** | 26.00 | 24.27 | 25.14 |
| **ResNet50** | **71.00** | **78.64** | **74.82** |
| **VGG16** | 52.50 | 55.83 | 54.17 |
### 📌 Self-Collected Dataset Performance (With Preprocessing)
| Model | Accuracy 1 (%) | Accuracy 2 (%) | Average (%) |
|---------|--------------|--------------|------------|
| **CNN** | 18.00 | 12.62 | 15.31 |
| **ResNet50** | **53.00** | **55.83** | **54.42** |
| **VGG16** | 47.50 | 58.25 | 52.88 |
#### 🔗 Self-Collected Dataset Used (Dataset 1)
> Refer to the end of this README.
## 🛠️ Installation Guide
### 📌 Prerequisites
Ensure you have the following installed:
- 🐍 **Python 3.9+**
### ⚙️ Steps to Install and Run
1️⃣ **Clone the repository** 🖥️
```bash
git clone https://github.com/rydzze/Ichigo-Fruit-Classifier.git
cd Ichigo
```
2️⃣ **Install dependencies** 📦
```bash
pip install -r requirements.txt
```
3️⃣ **Run the application**
```bash
python run.py
```
4️⃣ **Access the system:** 🌍
```
http://localhost:686
```
## 📸 Screenshots of User Interface
## 🏆 **Contribution**
We would like to express our gratitude to the following individuals for their contributions to Ichigo:
- [Muhammad Ariff Ridzlan](https://github.com/rydzze)
- [Muhammad Hafiz](https://github.com/IbnAsmuni)
- [Siti Nur Aisyah](https://github.com/ayesharizani)
- [Nurul Hurul Aini](https://github.com/ainiharis)
Your dedication and expertise have been instrumental in the development of this system. 🚀💡
## 💻 Google Drive Link (Alternative)
> [Ichigo](https://drive.google.com/drive/folders/1AcXk4rswDTDdUeTO1zVb6PG-ohWoQWai?usp=sharing), including the self-collected dataset and .h5 model files.