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Multi-objective monitoring method of yellow-feathered broiler behavior based on GMA-YOLO11n model
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Hongfei CHEN1, Sunyuan WANG1, Jiaqi YU1, Junling LIU2, Maohua XIAO3, Yan QIAN1, Xiuguo ZOU1
Journal of Intelligent Agricultural Mechanization | 2026, 7(1) : 43 - 51
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Journal of Intelligent Agricultural Mechanization | 2026, 7(1): 43-51
Multi-objective monitoring method of yellow-feathered broiler behavior based on GMA-YOLO11n model
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Hongfei CHEN1, Sunyuan WANG1, Jiaqi YU1, Junling LIU2, Maohua XIAO3, Yan QIAN1, Xiuguo ZOU1
Affiliations
  • 1.College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 211800, China
  • 2.Jiangsu Shennong Intelligent Technology Co., Ltd., Nanjing 211800, China
  • 3.College of Engineering, Nanjing Agricultural University, Nanjing 211800, China
  • CHEN Hongfei, E-mail:

About Author:

CHEN Hongfei, E-mail:

Published: 2026-02-15 doi: 10.12398/j.issn.2096-7217.2026.01.005
Outline
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Timely identification of abnormal conditions in broiler chickens through routine inspections is a crucial means to improve the efficiency of intensive poultry farming management. In intensive poultry farming, computer vision-based inspection technologies outperform traditional manual methods in both accuracy and efficiency, showing significant potential for development. However, in large-scale farms, challenges such as multi-scale targets and occlusion pose considerable difficulties for models. To address these issues, this study proposes an improved object detection method, termed GMA-YOLO11n (GSConv and multi-scale attention YOLO11n), based on the YOLO11n (you only look once) framework. Specifically, a GSConv lightweight convolution module is introduced into the Backbone to reduce computational complexity, and a high-resolution feature layer of 160×160 is added through multi-scale feature fusion to enhance the detection of small-scale and densely distributed targets. In addition, a squeeze-and-excitation (SE) channel attention module is incorporated before multi-scale feature inputs to strengthen the representation of key features. Experimental results demonstrate that the proposed model can effectively perform multi-class detection of daily behaviors, including drinking, feeding, and walking, as well as abnormal states of broiler chickens. The model achieves mean average precision values of 93.87% and 90.45% on Dataset I and Dataset Ⅱ , respectively, both outperforming the baseline model, while maintaining an inference speed that satisfies practical video inspection requirements.

yellow-feathered broilers  /  behavior detection  /  YOLO11n  /  attention mechanism  /  multi-scale features
陈虹菲, 王孙缘, 于佳琪, 刘俊岭, 肖茂华, 钱燕, 邹修国. 基于GMA-YOLO11n的黄羽肉鸡行为多目标检测方法研究. 智能化农业装备学报(中英文), 2026 , 7 (1) : 43 -51 . DOI: 10.12398/j.issn.2096-7217.2026.01.005
Hongfei CHEN, Sunyuan WANG, Jiaqi YU, Junling LIU, Maohua XIAO, Yan QIAN, Xiuguo ZOU. Multi-objective monitoring method of yellow-feathered broiler behavior based on GMA-YOLO11n model[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (1) : 43 -51 . DOI: 10.12398/j.issn.2096-7217.2026.01.005
  • Jiangsu Provincial International Cooperation Project(BZ2023013)
  • Sub-project of the National Key Research and Development Program of China(2024YFD200030204)
Year 2026 volume 7 Issue 1
28
5
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Article Info
doi: 10.12398/j.issn.2096-7217.2026.01.005
  • Receive Date:2025-12-02
  • Online Date:2026-11-03
  • Published:2026-02-15
Article Data
Affiliations
History
  • Received:2025-12-02
  • Revised:2026-01-12
Funding
Jiangsu Provincial International Cooperation Project(BZ2023013)
Sub-project of the National Key Research and Development Program of China(2024YFD200030204)
Affiliations
    1.College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 211800, China
    2.Jiangsu Shennong Intelligent Technology Co., Ltd., Nanjing 211800, China
    3.College of Engineering, Nanjing Agricultural University, Nanjing 211800, China

Corresponding:

* ZOU Xiuguo, E-mail:
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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