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智能化农业装备学报(中英文) ›› 2023, Vol. 4 ›› Issue (2): 22-34.DOI: 10.12398/j.issn.2096-7217.2023.02.003

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猪专用传感器研究进展

杨亮1王辉1陈睿鹏1盛清凯2袁震3熊本海1*   

  1. 1. 中国农业科学院北京畜牧兽医研究所 动物营养学国家重点实验室,北京市,100193;
    2. 山东省农业科学院畜牧兽医研究所,山东济南,250199; 3. 山东鑫基牧业有限公司,山东泰安,271299
  • 出版日期:2023-05-15 发布日期:2023-05-15
  • 通讯作者: 熊本海,男,1963年生,湖北红安人,博士,研究员;研究方向为畜牧信息与装备。E-mail: xiongbenhai@caas.cn
  • 作者简介:杨亮,男,1980年生,吉林四平人,博士,研究员;研究方向为智慧畜牧业。E-mail: yangliang@caas.cn
  • 基金资助:
    山东省重点研发计划课题(2022TZXD0016);国家重点研发计划课题(2021YFD2000804)

Advances in pig-specific sensors

YANG Liang1, WANG Hui1, CHEN Ruipeng1, SHENG Qingkai2YUAN Zhen3, XIONG Benhai1*#br#   

  1. 1. State Key Laboratory of Animal Nutrition, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; 2. Institute of Animal Husbandry and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250199, China; 3. Shandong Xinji Animal Husbandry Co., Ltd., Tai'an 271299, China
  • Online:2023-05-15 Published:2023-05-15

摘要: 传统养猪业存在人工成本高、养殖效率低、工作强度大等缺点,制约了农业现代化的发展。以规模化、集约化、数字化为导向的养殖模式成为精准畜牧业发展的必然要求,基于专业传感器的个体养殖和健康监测技术成为研究的主要方向。从行为传感器、生长生理传感器、疾病检测传感器3个方面,介绍猪专用传感器的研究进展。非洲猪瘟的暴发,增加了对传感器技术的需求,加快了新传感方法的发展。在评估家畜的适应性生理方面,传感器技术因其可以获取行为和生理数据的时间序列,对测量家畜的生理参数至关重要。生物传感器和可穿戴设备,基于先进的统计和计算机科学方法,用于预测和评估家畜的适应性反应和复原力。声音、图像、视频等实时分析动物的体况数据,可改善牲畜的生物学指标。未来传感器技术的发展将有利于养殖户全面了解动物的健康和福利状况。传感器设备将逐渐从接触性向非接触性发展,减少对动物的心理压力。在行为监测方面,视频监测通过远距离的目标跟踪避免传统可穿戴设备对动物的影响问题。在动物个体识别算法的可靠性方面,实现多目标个体的精准识别将是研究重点。在动物行为检测算法的适用性方面,动物个体的复杂行为研究将成为研究的重点方向。在动物个体疾病预测方面,重点是实现对猪只生理反应的早期识别,提高动物健康与福利水平。本研究为改善动物健康和福利、提高动物生产力、降低生产成本并最大限度地减少环境污染,为社会创造价值提供参考。

关键词: 猪, 传感器, 行为, 生长生理, 疾病检测

Abstract: Traditional pig farming has disadvantages such as high labor costs, low breeding efficiency, and high work intensity, which restrict the development of agricultural modernization. The scale, intensive and digital-oriented breeding model has become an inevitable requirement for the development of precision animal husbandry, and individual farming and health monitoring technology based on professional sensors has become the main direction of research. This paper introduces the research progress of pig-specific sensors from three aspects: behavioural sensors, growth and physiological sensors, and disease detection sensors. The outbreak of African swine fever has increased the demand for sensor technology and accelerated the development of new sensing methods. In assessing the adaptive physiology of livestock, sensor technology is essential for measuring physiological parameters of livestock due to its ability to capture time series of behavioural and physiological data. Biosensors and wearable technologies, based on advanced statistical and computer science methods, are used to predict and assess adaptive responses and resilience of livestock. Real-time analysis of animal body condition data such as sound, images and video can improve the biological indicators of livestock. Future developments in sensor technology will facilitate farmers to gain a comprehensive understanding of the health and welfare of their animals. Sensor devices will gradually move from contact to non-contact, so as to reduce he psychological stress on the animals. In terms of behavioural monitoring, video monitoring avoids the problem of traditional wearable devices affecting animals through long-distance target tracking. In terms of the reliability of individual animal identification algorithms, achieving accurate identification of multiple target individuals will be the focus of research. In terms of the applicability of animal behaviour detection algorithms, the study of the complex behaviour of individual animals will be a key research direction. In terms of individual animal disease prediction, the focus will be on achieving early identification of physiological responses in pigs and improving animal health and welfare. This study introduces the purpose of pig sensors to create value for society by improving animal health and welfare, increasing animal productivity, reducing production costs, and minimizing environmental pollution.

Key words: pigs, sensors, behaviour, growth physiology, disease detection

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