Aggressive behavior recognition and welfare monitoring in yellow-feathered broilers using FCTR and wearable identity tags

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Hongcheng Xue , Jie Ma , Yakun Yang , Hao Qu , Longhe Wang , Lin Li
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引用次数: 0

Abstract

Aggressive behavior and individual identification in chickens have long attracted widespread attention in animal welfare farming and scientific genetic breeding. Existing methods predominantly rely on manual observation, which is limited by subjectivity and slow response times. The matching of chicken identity with behavior requires considerable human and material resources. To address these challenges, we propose a Fast Chicken aggressive behavior recognition model based on TRansformer (FCTR) model and introduce a wearable identity tag for chickens. FCTR demonstrates robust recognition performance on the free-range Yellow-Feathered Broiler dataset and establishes an identity matching verification method, refining behavioral quantification analysis at the individual level for precise farming. To evaluate this approach, the ChickenFight-2024 dataset was collected and constructed. Multiple experiments confirm that the method can effectively identify both chicken identities and aggressive behaviors using video surveillance images. The proposed model achieved mAP values of 89.81%, 85.76%, 90.14%, 93.19%, and 87.27% for fight, tread, peck, eat, and drink behaviors, respectively, with an mAP of 77.39% for identity information. The identity matching verification method achieved a 94.88% matching rate, highlighting the model’s significant potential for application in commercial farming scenarios and offering new insights and solutions for efficient genetic breeding.
利用 FCTR 和可穿戴身份标签识别黄羽肉鸡的攻击行为并进行福利监测
长期以来,鸡的攻击行为和个体识别在动物福利养殖和科学遗传育种中引起了广泛关注。现有的方法主要依赖人工观察,而人工观察受到主观性和反应时间慢的限制。将鸡的身份与行为相匹配需要大量的人力和物力。为了应对这些挑战,我们提出了一种基于 TRansformer(FCTR)模型的快速鸡攻击行为识别模型,并引入了一种可穿戴的鸡身份标签。FCTR 在散养黄羽肉鸡数据集上表现出强大的识别性能,并建立了身份匹配验证方法,完善了个体层面的行为量化分析,实现了精准养殖。为评估该方法,收集并构建了 ChickenFight-2024 数据集。多项实验证实,该方法能利用视频监控图像有效识别鸡的身份和攻击行为。所提出的模型在斗鸡、踩鸡、啄鸡、吃鸡和喝鸡行为方面的 mAP 值分别为 89.81%、85.76%、90.14%、93.19% 和 87.27%,在身份信息方面的 mAP 值为 77.39%。身份匹配验证方法的匹配率高达 94.88%,凸显了该模型在商业化养殖场景中的巨大应用潜力,并为高效遗传育种提供了新的见解和解决方案。
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来源期刊
Computers and Electronics in Agriculture
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.
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