Detection and analysis of sow nursing behavior based on the number and location of piglets outside the suckling area using YOLOv5s

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2025-04-03 DOI:10.1016/j.compag.2025.110324

Luo Liu , Jinxin Chen , Qi-an Ding , Ruqian Zhao , Mingxia Shen , Longshen Liu

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引用次数: 0

Abstract

The nursing behavior of sows plays a crucial role in piglet growth, making precise monitoring and robust statistical analysis essential for a comprehensive evaluation of maternal characteristics. This study employed RGB cameras alongside an innovative Recognition Sow Nursing–You Only Look Once (RSN-YOLO) model to effectively monitor sow nursing behavior. The experimental comprised 24 sows of Yorkshire or Landrace breeds, each fostering no more than 13 piglets. The system automatically detected and recorded the start and end times of each nursing episode, enabling the collection and subsequent analysis of both individual sow characteristics and group behaviors. Results indicate that the YOLOv5s object detection model strikes an optimal balance between speed and accuracy, processing frames at 5.7 ms/frame, while achieving a precision rate of 96.3 %, a recall rate of 95.0 %, and a mean Average Precision ([email protected]) of 97.3 %. Comparisons between these automated detections and manual counts from continuous 24-hour video recordings across five pens confirmed that the method accurately captures both the number of nursing instances and the total duration of nursing with over 95 % accuracy when count errors do not exceed two occurrences. Even when count errors exceed two, accuracy remains above 92 %, with the average duration of each nursing session consistently measured with high precision. The study further revealed that sow nursing behavior does not exhibit a significant day-night rhythm, although notable individual variability within the group is evident. This variability is critical for early identification and intervention in cases where sows exhibit abnormal nursing behaviors relative to overall group patterns. Over a 21-day lactation period, both the total and daily average nursing durations decreased and subsequently stabilized as piglets aged, while the frequency of nursing remained relatively constant. A notable positive correlation (r = 0.67) was found between the number of nursing events and the total nursing duration. Additionally, the results support previous findings that proximally influences the synchronicity of nursing behavior: sows located farther apart are significantly less likely to nurse simultaneously (P < 0.01). Overall, this methodology introduces a novel approach for automating the monitoring of sow nursing behavior on large-scale pig farms. By analyzing individual and group nursing patterns, the approach facilitates the early detection and warning of abnormal nursing behaviors, thereby enhancing the assessment of sow nursing performance and significantly advancing precision livestock farming.

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利用YOLOv5s基于哺乳区外仔猪数量和位置的母猪哺乳行为检测与分析

母猪的护理行为在仔猪生长中起着至关重要的作用，因此精确的监测和可靠的统计分析对于全面评估母猪的特征至关重要。本研究采用RGB相机和创新的识别母猪护理-你只看一次（RSN-YOLO）模型来有效监测母猪的护理行为。试验选用24头约克郡或长白猪，每头不超过13头仔猪。该系统自动检测并记录每次哺乳的开始和结束时间，从而能够收集和随后分析母猪的个体特征和群体行为。结果表明，YOLOv5s目标检测模型在速度和精度之间达到了最佳平衡，处理帧的速度为5.7 ms/帧，准确率为96.3%，召回率为95.0%，平均平均精度（[email protected]）为97.3%。将这些自动检测与5个围栏连续24小时视频记录的人工计数进行比较，证实了当计数错误不超过两次时，该方法准确捕获护理实例的数量和护理总持续时间，准确率超过95%。即使计数误差超过2，准确率仍保持在92%以上，每次护理会议的平均持续时间始终以高精度测量。研究进一步表明，母猪的哺乳行为不表现出明显的昼夜节律，尽管组内显著的个体差异是明显的。在母猪表现出相对于整体群体模式的异常护理行为的情况下，这种可变性对于早期识别和干预至关重要。在21天的哺乳期中，随着仔猪年龄的增长，总哺乳时间和日平均哺乳时间均减少并趋于稳定，而哺乳频率保持相对稳定。护理事件次数与总护理时间呈显著正相关（r = 0.67）。此外，研究结果支持了先前的研究结果，即近端影响护理行为的同步性：距离较远的母猪同时哺乳的可能性显着降低(P <；0.01)。总体而言，该方法为大规模养猪场的母猪护理行为自动化监测引入了一种新颖的方法。该方法通过分析个体和群体的护理模式，有助于早期发现和预警异常的护理行为，从而提高对母猪护理绩效的评估，显著推进畜禽精准养殖。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.