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Study On Ruminant Behavior Analysis Of Beef Cattle In Shelter Feeding

Posted on:2020-07-12Degree:MasterType:Thesis
Country:ChinaCandidate:T J YangFull Text:PDF
GTID:2393330590988445Subject:Agricultural Electrification and Automation
Abstract/Summary:PDF Full Text Request
The ruminant behavior of beef cattle is a big sign of its physiological characteristics and can directly reflect the current health status of beef cattle.Ruminant behavior as the main daily behavior of beef cattle,in addition to reflect the health conditions,but also shows the beef feed nutrition ratio,degree of recovery,the best time to slaughter and other information.Therefore,this paper designs a ruminant behavior monitoring system for beef cattle,which is used to collect information about ruminant behavior of beef cattle.The BP neural network and support vector machine method are used to realize the recognizing ruminant behavior of beef cattle,and then to study the ruminant behavior of beef cattle.The main contents of this article are as follows:(1)the overall planning and design of the way of the daily behavior of beef cattle was carried out,and a beef cattle behavior monitoring system was constructed.According to the corresponding hardware and software requirements,suitable equipment was selected for use in the system.This article selects the bluetooth movement sensor to transmit the collected data to the upper computer through the wireless bluetooth transmission way,carries on the data recording and the preservation on the PC end,thus obtains the continuous,complete beef cattle daily behavior information.(2)The behavioral information of the healthy beef cattle and Minhang beef cattle were classified and identified by BP neural network.Prior to this,in order to ensure the authenticity of the data,the selected data was denoised by using db wavelet;then the data was classified into specific intervals based on standard normalization,and the mutual influence between the various quantities was reduced to restore the real data.Subsequently,the input layer,the output layer and the number of hidden layer nodes are determined by the experimental characteristics,the transfer function is established,the BP neural network model is established,the pre-processed behavior data is brought into the model,and the ruminant behavior is classified and studied,thereby obtaining the beef cattle at this time.The state of behavior.The results showed that the accuracy of BP neural network for recognizing the ruminant behavior of healthy beef cattle was 86.29%,and the accuracy of ruminant behavior of Minhang beef cattle was 81.49%.That is,the health status of the beef cattle has a certain influence on the test results.(3)Based on the analysis of behavior information of healthy beef cattle and lame beef cattle using two-class support vector machine algorithm,it was found that the correct rate ofrecognition of ruminant behavior of healthy beef cattle by two-class support vector machine was 89.71%,and that of lame beef cattle was 90.02%.The results show that support vector machine is more suitable than BP neural network for classification and recognition of beef ruminant behavior data selected in this paper,and the results are more accurate.At the same time,it also confirms that the effect of healthy beef cattle on the test results is relatively less than that of lame beef cattle on the test results.Therefore,on the basis of two-class support vector machine,multi-class support vector machine is used to further refine the daily behavior of healthy beef cattle,which can be divided into four categories: standing rumination,lying rumination,standing non-rumination and lying non-rumination.Binary decision tree support vector machine was used to identify the behavior of beef cattle,and finally the results of behavior classification were obtained.The results showed that the accuracy rates of standing rumination,lying rumination,standing non-rumination and lying non-rumination were 89.78%,91.56%,94.22% and 96.89%,respectively.The rumination behaviors of beef cattle were classified correctly.In summary,the Bluetooth motion sensor selected in this paper can completely collect the daily behavior information of beef cattle,and use different algorithms to achieve the purpose of classifying and identifying the daily behavior of beef cattle.The use of sensors,wireless transmission technologies, achieve the purpose of standardized,intelligent,automated,digital breeding methods,for the future ruminant livestock in the field of behavior identification,can provide theoretical support and data support for the cattle industry Play a good guiding role.
Keywords/Search Tags:Feeding beef cattle, Bluetooth sensor, Rumination behavior, BP neural network, Support vector machine
PDF Full Text Request
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