Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer Chickens

Hengtai Li, Hongfei Chen, Jinlin Liu, Qiuhong Zhang, Tao Liu, Xinyu Zhang, Yuhua Li, Yan Qian and Xiuguo Zou ()
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Hengtai Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Hongfei Chen: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Jinlin Liu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Qiuhong Zhang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Tao Liu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Xinyu Zhang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yuhua Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yan Qian: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Xiuguo Zou: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2025, vol. 15, issue 11, 1-21

Abstract: With the increasing urgency for digital transformation in large-scale caged layer farms, traditional methods for monitoring the environment and chicken health, which often rely on human experience, face challenges related to low efficiency and poor real-time performance. In this study, we focused on caged layer chickens and proposed an improved abnormal beak detection model based on the You Only Look Once v8 (YOLOv8) framework. Data collection was conducted using an inspection robot, enhancing automation and consistency. To address the interference caused by chicken cages, an Efficient Multi-Scale Attention (EMA) mechanism was integrated into the Spatial Pyramid Pooling-Fast (SPPF) module within the backbone network, significantly improving the model’s ability to capture fine-grained beak features. Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. The model was deployed on edge computing devices for the real-time detection of abnormal beak features in layer chickens. Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. This innovative approach not only improves the extraction of subtle features but also addresses occlusion challenges commonly encountered in small target detection. Experimental results demonstrate that the improved model achieved a detection accuracy of 92.7%. In terms of the comprehensive evaluation metric (mAP), it surpassed the baseline model and YOLOv5 by 2.4% and 3.2%, respectively. The digital twin system also proved stable in real-world scenarios, effectively mapping physical conditions to virtual environments. Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming.

Keywords: beak deformity detection; deep learning; visual technology; digital twin; layer chickens (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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