Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

Saifullah, Shoffan and Drezewski, Rafal and Yudhana, Anton and Pranolo, Andri and Kaswijanti, Wilis and Suryotomo, Andiko Putro and Putra, Seno Aji and Khaliduzzaman, Alin and Prabuwono‬, Anton Satria and Japkowicz, Nathalie (2023) Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 9 (3). pp. 854-871.

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Abstract

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate classification.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	20 Sep 2023 06:27
Last Modified:	20 Sep 2023 06:27
URI:	http://eprints.uad.ac.id/id/eprint/50388

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