【摘要】：為進(jìn)一步加強對反芻類家畜的規(guī)范化飼養(yǎng),合理利用資源,提高生產(chǎn)品質(zhì),研究其日常行為規(guī)律便成了關(guān)鍵因素。舍飼肉牛的反芻行為是反映肉牛健康狀態(tài)的重要指標(biāo)之一。為研究肉牛的反芻行為規(guī)律,在遼寧未來牧業(yè)隨機抽取50頭處于育肥期的西門塔爾健康肉牛作為試驗對象,進(jìn)行為期7d的日�；顒佑^察,并記錄其行為。將50頭肉牛分為試驗組和對照組,每組25頭。在試驗組肉牛的額頭綁上九軸藍(lán)牙動作傳感器,用于采集肉牛在x,y,z軸上的加速度,角速度和角度共9組特征向量數(shù)據(jù)。通過小波降噪對此特征向量進(jìn)行預(yù)處理,選擇標(biāo)準(zhǔn)歸一化的方法降低數(shù)據(jù)間相互的綱量和影響。抽取特征向量中的70%作為訓(xùn)練集投入SVM(support vector machine,支持向量機)進(jìn)行二分類訓(xùn)練得到反芻模型,隨后將其余30%作為測試集帶入反芻模型進(jìn)行分類預(yù)測,以判斷是否存在反芻行為。最后,利用對照組的肉牛與試驗組相比對,判斷試驗組的肉牛是否出現(xiàn)異常行為。試驗結(jié)果表明:所采用試驗設(shè)備工作正常,信號傳輸穩(wěn)定;舍飼肉牛在佩戴傳感器節(jié)點過程中無不良反應(yīng)。SVM可以通過藍(lán)牙動作傳感器發(fā)送得到的特征向量高效且準(zhǔn)確的判斷肉牛的反芻行為與非反芻行為。分類結(jié)果達(dá)到97.7728%(其中,反芻識別率97.659%,非反芻識別率97.667%),滿足對反芻行為分類的目的 ,可有效的識別反芻行為。
[Abstract]:In order to further strengthen the standardized feeding of ruminant livestock, rationally utilize resources and improve the quality of production, the study of their daily behavior has become a key factor. Fifty beef cattle were divided into experimental group and control group, 25 in each group. Nine-axis Bluetooth motion sensors were attached to the forehead of beef cattle in the experimental group to collect the acceleration, angular velocity and angle of beef cattle on x, y, Z axes. Eigenvector data. wavelet denoising is used to preprocess the eigenvector, and standard normalization method is selected to reduce the dimension and influence between the data. 70% of the eigenvectors are extracted as training set and put into SVM (support vector machine) for binary training to get the rumination model. The remaining 30% is used as test. Finally, the beef cattle in the control group were compared with the experimental group to determine whether abnormal behavior occurred in the experimental group. The results showed that the experimental equipment was working normally and the signal transmission was stable. SVM can effectively and accurately judge ruminant behavior and non-ruminant behavior of beef cattle by feature vectors sent by Bluetooth motion sensor. The classification result reaches 97.7728% (of which, the recognition rate of ruminant is 97.659%, the recognition rate of non-ruminant is 97.667%). SVM can meet the purpose of classification of ruminant behavior and can effectively identify ruminant behavior.
【作者單位】： 沈陽農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院;國網(wǎng)大連供電公司;
【基金】：遼寧省科技廳自然科學(xué)基金項目(2015020760)
【分類號】：S823

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