刘忠超,何东健.基于卷积神经网络的奶牛发情行为识别方法[J].农业机械学报,2019,50(7):186-193.
LIU Zhongchao,HE Dongjian.Recognition Method of Cow Estrus Behavior Based on Convolutional Neural Network[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(7):186-193.
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基于卷积神经网络的奶牛发情行为识别方法   [下载全文]
Recognition Method of Cow Estrus Behavior Based on Convolutional Neural Network   [Download Pdf][in English]
投稿时间:2019-04-01  
DOI:10.6041/j.issn.1000-1298.2019.07.019
中文关键词:  奶牛  发情  图像识别  深度学习  爬跨行为  卷积神经网络
基金项目:国家重点研发计划项目(2017YFD0701603)和国家自然科学基金面上项目(61473235)
作者单位
刘忠超 西北农林科技大学
南阳理工学院 
何东健 西北农林科技大学 
中文摘要:对奶牛发情的及时监测在奶牛养殖中至关重要。针对现有人工监测奶牛发情行为费时费力、计步器接触式监测会产生奶牛应激行为等问题,根据奶牛发情的爬跨行为特征,提出一种基于卷积神经网络的奶牛发情行为识别方法。构建的卷积神经网络通过批量归一化方法提高网络训练速度,以Max-pooling为下采样,修正线性单元(Rectified linear units,ReLU)为激活函数,Softmax回归分类器为输出层,结合理论分析和试验验证,确定了32×32-20c-2s-50c-2s-200c-2的网络结构和参数。经过对奶牛活动区50头奶牛6个月的视频监控,筛选了具有发情行为爬跨特征的视频150段,随机选取网络训练数据23000幅和测试数据7000幅,对构建的网络进行了训练和测试。试验结果表明:本文方法对奶牛发情行为识别准确率为98.25%,漏检率为5.80%,误识别率为1.75%,平均单幅图像识别时间为0.257s。该方法能够实现奶牛发情爬跨的无接触实时监测,对奶牛发情行为具有较高的识别率,可显著提高规模化奶牛养殖的管理效率
LIU Zhongchao  HE Dongjian
Northwest A&F University;Nanyang Institute of Technology and Northwest A&F University
Key Words:cow  estrus  image recognition  deep learning  span behavior  convolutional neural network
Abstract:Timely monitoring of cow estrus is very important in dairy cow breeding. At present, artificial estrus monitoring of dairy cows is time-consuming and laborious. Pedometer contact monitoring can easily cause stress discomfort to cows. Aiming at the problems existing in cow estrus monitoring, according to cows span behavior characteristics during oestrus, a method of cow’s oestrus behavior recognition based on convolutional neural network (CNN) was proposed. The convolution neural network was constructed to improve the network training speed by batch normalization. Max-pooling was used as the down sampling, rectified linear units (ReLU) was used as the activation function, and softmax regression classifier was used as the output layer. Through the theoretical analysis and experimental verification, the network structure and parameters of 32×32-20c-2s-50c-2s-200c-2 were designed. Through video surveillance of dairy cow activity area, 150 video segments with oestrus span behavior were extracted from 50 cows behavior videos within 6 months. The network training data of 23000 frames and the test data of 7000 frames were randomly selected from selected video segments, which were used to train and test the CNN. The results showed that the recognition accuracy of estrus behavior in dairy cows was 98.25%, the missed detection rate was 5.80%, the false recognition rate was 1.75%, and the average recognition time of single frame image detection was 0.257s. It proved that the method could realize the contactless real time monitoring of the cow’s estrus span behavior and had a high recognition rate for cow estrus. It can significantly improve the management efficiency of large-scale farming, and had a good application prospect.

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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