Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.
Subjects: Biology >> Zoology submitted time 2018-12-24 Cooperative journals: 《动物营养学报》
Abstract：本试验分析了新疆南疆地区肉羊常用精饲料体外产气量与有效降解率的相关性，旨在寻找肉羊常用精饲料干物质有效降解率的简易评价方法。以新疆南疆地区肉羊常用的6种蛋白质饲料和7种能量饲料为试验材料，采用体外产气法与人工瘤胃持续发酵法，在测定产气量（GP）与干物质有效降解率的同时，分析二者之间的相关性及常规营养成分对干物质有效降解率的影响，建立了以GP、常规营养成分预测干物质有效降解率的模型。结果显示：1）潜在产气量及8、16、24、36、48 h产气量均与干物质有效降解率呈正相关关系（P<0.05）；以相关性最强的3个预测值潜在产气量及24和36 h产气量为预测因子，分别建立干物质有效降解率的预测模型，其决定系数（R2）分别为0.553、0.613和0.612。2）饲料常规营养成分含量与干物质有效降解率的相关性分析结果中，干物质有效降解率与饲料中性洗涤纤维（NDF）含量呈显著负相关关系（P<0.05），与饲料酸性洗涤纤维（ADF）含量呈极显著负相关关系（P<0.01）；建立以ADF、NDF含量预测饲料干物质有效降解率的一元、二元预测模型，二元预测模型[EDIVDMD=88.481-0.484ADF-0.231NDF（P<0.01）（EDIVDMD为干物质有效降解率）]R2最高，为0.855。综合得出,体外产气法可以代替尼龙袋法预测肉羊常用精饲料的干物质有效降解率，可对饲料的降解性能做出快速、合理、有效的评价；利用饲料中的纤维含量来预测其他主要营养物质在瘤胃内降解率的方法切实可行。
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