Fertile Egg Detector [CH2-PS266]

Bangkit Batch 2 2023

Designing a Deep Learning-Based Mobile Application for Egg Candling

Theme: Food Accessibility, Agribusiness, and Food Security

Team Member

1. (ML) M466BSX1032 – Brilian Herda – Universitas Sains Al-Qur’ an - [Active]
2. (ML) M466BSY1195 – Ahmad Ma'ruf – Universitas Sains Al-Qur’an - [Active]
3. (ML) M123BSY1353 – Habibi Ahmadi Muslim – Politeknik Elektronika Negeri Surabaya - [Active]
4. (CC) C182BSY3830 – Fathin Cahyo Ramadhan – Universitas Amikom Purwokerto - [Active]
5. (CC) C182BSY3969 – Yuntafa Ulkhaq – Universitas Amikom Purwokerto - [Active]
6. (MD) A182BSY2566 – Sandhya Nugraha Qusnur Aulia - Universitas Amikom Purwokerto - [Active]
7. (MD) A548BKY4459 - Zulfahmi M. Ardianto - UIN Sunan Kalijaga Yogyakarta - [Active]

In this project, we've leveraged a cloud-based machine learning model for egg classification. This model, trained on diverse egg stage data, enables real-time analysis of images from the camera or gallery within the mobile app. It accurately categorizes eggs into early, mid, late, or non-viable stages, enhancing its functionality significantly.

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Notebook file:

Egg_Img_Classification.ipynb

Dataset: egg-candling-dataset