Autonomous inspection robot for dead laying hens in caged layer house

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-11-09 DOI:10.1016/j.compag.2024.109595

Weihong Ma , Xingmeng Wang , Simon X. Yang , Xianglong Xue , Mingyu Li , Rong Wang , Ligen Yu , Lepeng Song , Qifeng Li

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Abstract

Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the battery cages. To streamline this process, a specialized robot has been developed to enhance inspection efficiency, reduce manual labor, and enable rapid identification of dead hens. This inspection robot integrates cutting-edge technologies such as deep learning for real-time detection and identification, QR code-based positioning for precise localization, and autonomous navigation for seamless movement through the farm. It automates the otherwise tedious inspection process by visualizing and pinpointing the location of dead hens within the cages. In experimental tests, the robot achieved a detection accuracy of 90.61 % by incorporating a supplementary lighting system, setting an inspection speed of 9 m per minute, and fine-tuning the inspection algorithm with a probability value parameter of 0.48 and an area ratio parameter of 0.05. Additionally, the robot demonstrated a low false detection rate of 0.14 % and a minimal obvious false detection rate of 0.06 %. Compared to traditional manual inspection methods, this robotic system not only automates the task but also significantly reduces labor requirements and improves the overall management efficiency of large-scale egg farms. With its high accuracy and speed, the robot presents a viable solution for modern poultry operations, ensuring timely removal of dead hens and contributing to better farm hygiene and animal welfare.

Abstract Image

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用于检测笼养蛋鸡舍中死亡蛋鸡的自主检测机器人

在大规模蛋鸡养殖场中，对每只蛋鸡的日常检查既耗费人力又耗费时间，需要养殖场工作人员手动检查每只笼养母鸡，并及时清除任何死亡鸡只，以防止疾病在电池笼内传播。为了简化这一流程，我们开发了一种专用机器人，以提高检查效率，减少人工劳动，并能快速识别死亡母鸡。这种检查机器人集成了多项尖端技术，如用于实时检测和识别的深度学习技术、用于精确定位的基于二维码的定位技术，以及用于在农场内无缝移动的自主导航技术。它通过可视化和精确定位笼内死亡母鸡的位置，将原本繁琐的检查过程自动化。在实验测试中，该机器人通过安装辅助照明系统、设定每分钟 9 米的检测速度以及微调检测算法（概率值参数为 0.48，面积比参数为 0.05），实现了 90.61 % 的检测准确率。此外，机器人的误检率较低，仅为 0.14%，明显误检率最低，仅为 0.06%。与传统的人工检测方法相比，该机器人系统不仅实现了任务自动化，还大大减少了劳动力需求，提高了大规模蛋鸡养殖场的整体管理效率。凭借其高精度和高速度，该机器人为现代家禽饲养提供了一个可行的解决方案，确保及时清除死鸡，为改善农场卫生和动物福利做出贡献。

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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.