基于三轴加速度传感器的放牧羊只牧食行为研究  

Grazing Behavior of Herding Sheep Based on Three-axis Acceleration Sensor

作　　者：张春慧[1,2] 宣传忠 于文波[1,2] 郝敏 刘飞龙 ZHANG Chunhui;XUAN Chuanzhong;YU Wenbo;HAO Min;LIU Feilong(College of Mechanical and Electrical Engineering,Inner Mongolia Agricultural University,Huhhot 010018,China;Inner Mongolia Engineering Research Center for Intelligent Facilities in Grass and Livestock Breeding,Huhhot 010018,China)

机构地区：[1]内蒙古农业大学机电工程学院,呼和浩特010018 [2]内蒙古自治区草业与养殖业智能装备工程技术研究中心,呼和浩特010018

出　　处：《农业机械学报》2021年第10期307-313,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(31860666);内蒙古自治区自然科学基金项目(2017MS0606、2020BS06010)。

摘　　要：内蒙古自治区草地资源丰富,养羊业为自治区的主要畜牧业,通过对放牧羊只牧食行为的识别并结合GPS监测其牧食路径,可为估测放牧区域采食量分布、放牧规划和草畜平衡的研究提供理论依据。本文采用三轴加速度传感器,设计了放牧羊只牧食行为数据无线采集系统,自动采集羊只牧食的三轴加速度数据,并建立羊只牧食行为识别的BP神经网络模型、全连接深度神经网络模型和卷积神经网络模型,实现对羊只采食、咀嚼、反刍3种牧食行为的分类识别。在内蒙古自治区四子王旗白音朝克图镇半荒漠化草场的试验结果表明,BP神经网络模型、全连接深度神经网络模型和卷积神经网络模型对羊只牧食行为的平均识别率分别为83.1%、89.4%和93.8%,其中卷积神经网络模型的识别精度最高,能够满足羊只牧食行为分类识别的要求。Inner Mongolia is rich in grassland resources,and sheep industry is the main animal husbandry in the autonomous region.The intelligent identification of sheep grazing behaviors combined with GPS monitoring of grazing path can provide theoretical basis for estimating of feed intake distribution in grazing area,grazing planning and grassland-livestock balance.Three-axis acceleration sensors were used to design a wireless data acquisition system for grazing behaviors of herding sheep,and the system can automatically collect the three-axis acceleration data of grazing behaviors.BP neural network model,full connection deep neural network model and convolution neural network model were established to realize the classification and recognition of feeding,chewing and ruminating behaviors of herding sheep respectively.The experiments were carried out in a semi-desertification grassland in Inner Mongolia,and the natural grazing Mongolian sheep were selected as test objects.The results showed that the average recognition rates of BP neural network model,full connection deep neural network model and convolution neural network model were 83.1%,89.4%and 93.8%,respectively,and the convolution neural network model had the highest recognition accuracy,and the adaptability and stability of the network model was strong,which can meet the requirements of classification and recognition of sheep grazing behavior.The feeding path of sheep can be monitored by GPS.The research result can provide theoretical basis for ranch managers to formulate grazing system and improve grazing level.

关 键 词：羊 牧食行为 加速度传感器 神经网络 分类识别 

分 类 号：S24[农业科学—农业电气化与自动化]