Feeding behavior recognition of group-housed pigs based on pose estimation and keypoint features discrimination

IF 8.9 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-10-02 DOI:10.1016/j.compag.2025.111039

Yuhang Hu , Xin Dai , Baisheng Dai , Ran Li , Junlong Fang , Yanling Yin , Honggui Liu , Weizheng Shen

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引用次数: 0

Abstract

In the field of intelligent sensing for smart animal husbandry, accurate recognition of feeding behavior in group-housed pigs is crucial for achieving precision farming and improving pig welfare. Currently, pig feeding behavior recognition relies on detection boxes-based methods, which are difficult to exclude Non-Nutritive Visiting Behavior within the feeding zone. To precisely recognize the feeding behavior of group-housed pigs, this study proposes a feeding behavior recognition method based on pose estimation and keypoint features discrimination. Firstly, Pig-HRNet is designed to estimate the pose of group-housed pigs, in which a Context Transformer (COT) attention module is specially introduced to detect the keypoints of pigs more accurately under crowded conditions. Secondly, by analyzing the correlation between keypoints and feeding zone, group-housed pigs are divided into visiting the feeding zone and Non-Feeding Behavior (NFB). For visiting the feeding zone, the behaviors are further categorized into Feeding Behavior (FB) and Non-Nutritive Visiting Behavior (NNVB). The experimental data of group-housed pigs were collected in commercial pig farms, including a total of 1400 video frames. Experimental results show that the Pig-HRNet model achieves an average precision (AP) of 97.1% in estimating pig poses. Compared to other pose estimation network models such as KAPAO, HigherHRNet, DeepLabCut, and HRNet, the detection AP improved by 69.0%, 16.3%, 12.3%, and 0.5%, respectively. The feeding behavior recognition method proposed in this paper achieved precision and recall rates of 98.8% and 99.9%, respectively. The relevant results indicate that the proposed feeding behavior recognition method performs well, while also meeting the requirement for accurately estimating pig poses under crowded conditions. The feeding behavior dataset established in this paper has been shared on https://github.com/IPCLab-NEAU/Group-housed-pigs-Feeding-Behavior-Recognition for use by the precision animal husbandry research community.

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基于姿态估计和关键点特征判别的群养猪摄食行为识别

在智能畜牧业智能传感领域，准确识别群养猪的摄食行为对实现精准养殖和提高猪福利至关重要。目前，猪饲养行为识别依赖于基于检测盒的方法，难以排除饲养区内的非营养性探访行为。为了准确识别群养猪的摄食行为，本研究提出了一种基于姿态估计和关键点特征判别的摄食行为识别方法。首先，设计了Pig-HRNet来估计群养猪的姿态，其中特别引入了上下文转换（COT）注意力模块，在拥挤条件下更准确地检测猪的关键点。其次，通过分析关键点与饲喂区之间的相关性，将群养猪分为到访饲喂区和非饲喂行为（NFB）。在访问摄食区方面，进一步将行为分为摄食行为（FB）和非营养性访问行为（NNVB）。实验数据采集于商业养猪场群养猪，共1400帧视频。实验结果表明，猪hrnet模型估计猪姿态的平均精度达到97.1%。与KAPAO、HigherHRNet、DeepLabCut和HRNet等其他姿态估计网络模型相比，检测AP分别提高了69.0%、16.3%、12.3%和0.5%。本文提出的喂食行为识别方法的准确率和召回率分别达到98.8%和99.9%。实验结果表明，所提出的饲养行为识别方法在满足拥挤条件下准确估计猪位姿的要求的同时，具有良好的性能。本文建立的摄食行为数据集已在https://github.com/IPCLab-NEAU/Group-housed-pigs-Feeding-Behavior-Recognition上共享，供精密畜牧业研究界使用。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.