Computer Vision-Based Multi-Feature Extraction and Regression for Precise Egg Weight Measurement in Laying Hen Farms

Yunxiao Jiang, Elsayed M. Atwa, Pengguang He, Jinhui Zhang, Mengzui Di, Jinming Pan and Hongjian Lin ()
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Yunxiao Jiang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Elsayed M. Atwa: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Pengguang He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jinhui Zhang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mengzui Di: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Jinming Pan: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Hongjian Lin: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 19, 1-25

Abstract: Egg weight monitoring provides critical data for calculating the feed-to-egg ratio, and improving poultry farming efficiency. Installing a computer vision monitoring system in egg collection systems enables efficient and low-cost automated egg weight measurement. However, its accuracy is compromised by egg clustering during transportation and low-contrast edges, which limits the widespread adoption of such methods. To address this, we propose an egg measurement method based on a computer vision and multi-feature extraction and regression approach. The proposed pipeline integrates two artificial neural networks: Central differential-EfficientViT YOLO (CEV-YOLO) and Egg Weight Measurement Network (EWM-Net). CEV-YOLO is an enhanced version of YOLOv11, incorporating central differential convolution (CDC) and efficient Vision Transformer (EfficientViT), enabling accurate pixel-level egg segmentation in the presence of occlusions and low-contrast edges. EWM-Net is a custom-designed neural network that utilizes the segmented egg masks to perform advanced feature extraction and precise weight estimation. Experimental results show that CEV-YOLO outperforms other YOLO-based models in egg segmentation, with a precision of 98.9%, a recall of 97.5%, and an Average Precision (AP) at an Intersection over Union (IoU) threshold of 0.9 (AP90) of 89.8%. EWM-Net achieves a mean absolute error (MAE) of 0.88 g and an R 2 of 0.926 in egg weight measurement, outperforming six mainstream regression models. This study provides a practical and automated solution for precise egg weight measurement in practical production scenarios, which is expected to improve the accuracy and efficiency of feed-to-egg ratio measurement in laying hen farms.

Keywords: poultry production; computer vision; neural network; egg weight measurement (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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