Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer Chickens Article Swipe
Hengtai Li
,
Hongfei Chen
,
Jinglin Liu
,
Qiuhong Zhang
,
Tao Liu
,
Xinyu Zhang
,
Yuhua Li
,
Yan Qian
,
Xiuguo Zou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15111170
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15111170
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture15111170
- OA Status
- gold
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410859976
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410859976Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/agriculture15111170Digital Object Identifier
- Title
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Deep Learning-Based Detection and Digital Twin Implementation of Beak Deformities in Caged Layer ChickensWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-29Full publication date if available
- Authors
-
Hengtai Li, Hongfei Chen, Jinglin Liu, Qiuhong Zhang, Tao Liu, Xinyu Zhang, Yuhua Li, Yan Qian, Xiuguo ZouList of authors in order
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https://doi.org/10.3390/agriculture15111170Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/agriculture15111170Direct OA link when available
- Concepts
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Beak, Layer (electronics), Artificial intelligence, Biology, Computer science, Anatomy, Computer vision, Zoology, Materials science, NanotechnologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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