{"id":25726883,"url":"https://github.com/rydzze/ichigo-fruit-classifier","last_synced_at":"2026-05-08T06:15:32.735Z","repository":{"id":277265230,"uuid":"908865778","full_name":"rydzze/Ichigo-Fruit-Classifier","owner":"rydzze","description":"Project for Computer Vision","archived":false,"fork":false,"pushed_at":"2025-02-13T01:56:49.000Z","size":1876,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-13T02:35:40.400Z","etag":null,"topics":["computer-vision","deep-learning","fruit-classification","python","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rydzze.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-27T06:46:27.000Z","updated_at":"2025-02-13T01:56:52.000Z","dependencies_parsed_at":"2025-02-13T02:35:42.654Z","dependency_job_id":"155704e9-6fee-43da-ba06-630d088980bd","html_url":"https://github.com/rydzze/Ichigo-Fruit-Classifier","commit_stats":null,"previous_names":["rydzze/ichigo-fruit-classifier"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rydzze%2FIchigo-Fruit-Classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rydzze%2FIchigo-Fruit-Classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rydzze%2FIchigo-Fruit-Classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rydzze%2FIchigo-Fruit-Classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rydzze","download_url":"https://codeload.github.com/rydzze/Ichigo-Fruit-Classifier/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240762612,"owners_count":19853518,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","fruit-classification","python","tensorflow"],"created_at":"2025-02-25T23:27:44.658Z","updated_at":"2026-05-08T06:15:32.728Z","avatar_url":"https://github.com/rydzze.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🍓 Ichigo: Intelligent Multi-Fruit Classification and Quality Analysis System  \n\n## 📌 Introduction\n\nTraditional 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.  \n\n## ❗ Problem Statements  \n\nDespite advances in **computer vision and AI**, challenges persist in fruit classification:  \n\n🔸 **Manual inspection is prone to inconsistencies** – Human judgment can be subjective, leading to quality control issues.  \n🔸 **Limited datasets hinder classification accuracy** – AI models struggle with underrepresented fruit types.  \n🔸 **Scalability for real-time processing** – Many systems fail to handle large-scale, high-speed operations efficiently. \n\n## 🎯 Objectives  \n\nThe **Ichigo** system aims to:\n\n✅ **Enhance classification accuracy** using deep learning.  \n✅ **Enable real-time quality assessment** for efficient sorting.  \n✅ **Ensure scalability** for farms, supermarkets, and industries.   \n\n## 🔥 System Features  \n\n🚀 **Deep Learning-Based Classification** – Uses **CNN, ResNet-50, and VGG-16**.  \n🖥️ **Web-Based Interface** – Built with **Flask, HTML, CSS, and JavaScript**.  \n📊 **Data Preprocessing Techniques** – Image **enhancement, sharpening, and edge detection**.  \n🔍 **Real-Time Object Detection** – Segmentation and bounding box.  \n\n## 📊 Model Performance  \n\n### 📌 Public Dataset Performance (Without Preprocessing)  \n\n| Model   | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |  \n|---------|-------------|-------------|------------|-------------|  \n| **CNN** | 72.50      | 74.50       | 72.75      | 70.84       |  \n| **ResNet50** | **98.75** | **98.76** | **98.71** | **98.72** |  \n| **VGG16** | 97.03 | 97.08 | 96.92 | 96.96 |  \n\n### 📌 Public Dataset Performance (With Preprocessing)  \n\n| Model   | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |  \n|---------|-------------|-------------|------------|-------------|  \n| **CNN** | 72.81      | 73.01       | 72.70      | 71.88       |  \n| **ResNet50** | **99.22** | **99.21** | **99.20** | **99.20** |  \n| **VGG16** | 97.81 | 97.69 | 97.68 | 97.65 |  \n\n#### 🔗 Public Dataset Used\n\n\u003e 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)\n\n### 📌 Self-Collected Dataset Performance (Without Preprocessing)  \n\n| Model   | Accuracy 1 (%) | Accuracy 2 (%) | Average (%) |  \n|---------|--------------|--------------|------------|  \n| **CNN** | 26.00       | 24.27       | 25.14      |  \n| **ResNet50** | **71.00** | **78.64** | **74.82** |  \n| **VGG16** | 52.50 | 55.83 | 54.17 |  \n\n### 📌 Self-Collected Dataset Performance (With Preprocessing)  \n\n| Model   | Accuracy 1 (%) | Accuracy 2 (%) | Average (%) |  \n|---------|--------------|--------------|------------|  \n| **CNN** | 18.00       | 12.62       | 15.31      |  \n| **ResNet50** | **53.00** | **55.83** | **54.42** |  \n| **VGG16** | 47.50 | 58.25 | 52.88 |  \n\n#### 🔗 Self-Collected Dataset Used (Dataset 1)\n\n\u003e Refer to the end of this README.\n\n## 🛠️ Installation Guide  \n\n### 📌 Prerequisites  \n\nEnsure you have the following installed:  \n- 🐍 **Python 3.9+**  \n\n### ⚙️ Steps to Install and Run  \n\n1️⃣ **Clone the repository** 🖥️  \n\n```bash  \ngit clone https://github.com/rydzze/Ichigo-Fruit-Classifier.git\ncd Ichigo  \n```\n\n2️⃣ **Install dependencies** 📦  \n\n```bash  \npip install -r requirements.txt  \n```  \n\n3️⃣ **Run the application**  \n\n```bash  \npython run.py  \n```  \n\n4️⃣ **Access the system:** 🌍  \n\n```  \nhttp://localhost:686  \n```  \n\n## 📸 Screenshots of User Interface\n\n![image](https://github.com/user-attachments/assets/aff93e40-9f9b-4371-a68b-cf41996380c1)\n\n![image](https://github.com/user-attachments/assets/b8ee89c4-a8fe-41fb-9c69-4137f4cbf459)\n\n![image](https://github.com/user-attachments/assets/598c28ba-8d99-46b4-bad8-eec560420235)\n\n## 🏆 **Contribution**\n\nWe would like to express our gratitude to the following individuals for their contributions to Ichigo:\n\n- [Muhammad Ariff Ridzlan](https://github.com/rydzze)\n- [Muhammad Hafiz](https://github.com/IbnAsmuni)\n- [Siti Nur Aisyah](https://github.com/ayesharizani)\n- [Nurul Hurul Aini](https://github.com/ainiharis)\n\nYour dedication and expertise have been instrumental in the development of this system. 🚀💡\n\n## 💻 Google Drive Link (Alternative)\n\n\u003e [Ichigo](https://drive.google.com/drive/folders/1AcXk4rswDTDdUeTO1zVb6PG-ohWoQWai?usp=sharing), including the self-collected dataset and .h5 model files.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frydzze%2Fichigo-fruit-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frydzze%2Fichigo-fruit-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frydzze%2Fichigo-fruit-classifier/lists"}