International Journal on Science and Technology

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Call for Paper Volume 17 Issue 1 January-March 2026 Submit your research before last 3 days of March to publish your research paper in the issue of January-March.
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Volume 17 Issue 1
January-March 2026

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Deep Learning-based Assessment of Egg Quality: Freshness and Shell Integrity Detection

Author(s) Anto Remila S, Bhargvi Patel
Country India
Abstract Assessment of egg quality is a critical operation in the food industry to promote consumer safety and nutritional quality as well as preventing economic losses that are a direct result of spoilt products. Traditional ways of assessing the quality of eggs usually involve manual inspection that is subjective and demanding of labour as well as susceptible to human error. The most recent progress in computer vision and deep learning allows creating automated, non-invasive methods of identifying defects in eggs and assessing the quality of their shells.
The study uses the Good and Bad Eggs Identification Image Dataset of Mendeley Data, which has 1,000 high-resolution images of eggs and 6,000 augmented images created by transformation (flips, rotations, and brightness/contrast manipulation). The dataset records some important external characteristics of eggs such as shell texture, shape, color, and visible defects, thus, it is very appropriate in the defect classification and integrity detection processes.
Taking advantage of convolutional neural networks (CNNs) and transfer learning methods in Python through Google Colab, the present research will develop and test an automatic system that can differentiate between a good quality and a defective egg with high precision. This project results will also aid the development of intelligent-based food quality monitoring systems which can offer a scalable and more dependable solution to the issue of real-time monitoring of egg quality used in industry.
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Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-25
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