李丽华,刘志伟,赵学谦,李帅.基于加速度传感器的本交笼种鸡个体行为监测与识别[J].农业机械学报,2019,50(12):247-254.
LI Lihua,LIU Zhiwei,ZHAO Xueqian,LI Shuai.Monitoring and Identification of Natural Mating Cage Breeding Chickens Individual Behavior Based on Acceleration Sensor[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(12):247-254.
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基于加速度传感器的本交笼种鸡个体行为监测与识别   [下载全文]
Monitoring and Identification of Natural Mating Cage Breeding Chickens Individual Behavior Based on Acceleration Sensor   [Download Pdf][in English]
投稿时间:2019-07-12  
DOI:10.6041/j.issn.1000-1298.2019.12.028
中文关键词:  种鸡  本交笼  个体行为  加速度传感器  K means均值聚类算法
基金项目:国家重点研发计划项目(2018YFD0500702)、国家自然科学基金项目(31902209)和河北省二期现代农业产业技术体系创新团队建设项目(HBCT2018150208)
作者单位
李丽华 河北农业大学
农业农村部肉蛋鸡养殖设施工程重点实验室 
刘志伟 河北农业大学 
赵学谦 河北农业大学 
李帅 河北农业大学 
中文摘要:为了监测种鸡行为、了解鸡群的健康状况,实现本交笼养下种鸡个体行为的自动识别,设计了一种基于九轴加速度传感器和蓝牙无线传输的本交笼种鸡个体行为实时监测系统。采用小波sym降噪对原始数据预处理,根据不同行为的加速度曲线波动性提取加速度数据特征值,利用K-means聚类算法对行为特征进行识别,得到稳定的聚类中心;进行了加速度传感器读写距离测试和以充电宝、锂电池作为供电设备的供电情况对比;同时利用视频监控验证种鸡的5种行为。结果表明,该系统能够快速且连续不间断地采集本交笼种鸡个体行为信息,准确识别种鸡个体的多种行为,采食行为平均识别精度为94.31%,饮水行为平均识别精度为92.53%,打斗行为平均识别精度为84.03%,交配行为平均识别精度为72.00%,振翅行为平均识别精度为9231%。本研究有助于无损、快速识别种鸡个体行为,为本交笼养模式配套设施设备优化设计和高效管理提供科学依据。
LI Lihua  LIU Zhiwei  ZHAO Xueqian  LI Shuai
Hebei Agricultural University;Key Laboratory of Meat and Layer Breeding Facilities Engineering, Ministry of Agriculture and Rural Affairs,Hebei Agricultural University,Hebei Agricultural University and Hebei Agricultural University
Key Words:breeding chickens  natural mating cage  individual behavior  acceleration sensor  K means clustering algorithms
Abstract:Aiming to realize the automatic identification of individual behaviors of breeders in this natural mating cage, reduce the systematic error of observation and sampling, analyze breeding chicken behavior and understand the health condition of chicken group, a real time monitoring system based on nine axis acceleration sensor and bluetooth wireless transmission was designed to automatically monitor and quantify the individual behavior of breeding chickens in this cage. Wavelet sym was used to reduce noise influence on original data preprocessing. According to the fluctuation of different behaviors acceleration curve, the characteristic value of acceleration data was extracted and the K means clustering algorithm was used to identify behavioral features to obtain a stable clustering center. Distance comparison of acceleration sensor and performance test of portable battery and lithium battery as power supply equipment was done. Meanwhile, video was used to monitor and verify the five behaviors of breeding chickens. The system can quickly and continuously collect individual behavior information of breeding chickens in this cage, accurately identify individual feeding, drinking, fighting, mating and wing flapping behaviors. The average recognition accuracy of feeding was 94.31%, the average recognition accuracy of drinking was 92.53%, the average recognition accuracy of fighting was 84.03%, the average recognition accuracy of mating was 72.00%, and the average recognition accuracy of flapping was 92.31%. It was helpful to acquire individual behavior of breeding chickens without damage and quickly, and provide a scientific basis for optimal design and efficient management of facilities and equipment.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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