The objective of this study was to assess the effectiveness and practicability of an activityindex combining acceleration and location data for automated estrus detection in dairycows. By using a wearable neck tag, me...The objective of this study was to assess the effectiveness and practicability of an activityindex combining acceleration and location data for automated estrus detection in dairycows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity recordsof each cow. The maximum-minimum distance clustering (MMDC) method was used todivide hourly activity data into low, medium, high, and intensity level groups. The weightedsum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation inhourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The studyshowed that the power consumption and communication effects of the neck tags wereacceptable for indoor-housing conditions. For the two consecutive time periods, theactivity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision,100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. Onthe basis of these results, it can be concluded that the combination of acceleration andlocation in the activity index can promote estrus detection in dairy cows.展开更多
Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatur...Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.展开更多
To evaluate an artificial insemination program in postpartum Bos indicus cows and by using deterministic models, to estimate the possible economic advantages of insemination by appointment over detected estrus, sixty-...To evaluate an artificial insemination program in postpartum Bos indicus cows and by using deterministic models, to estimate the possible economic advantages of insemination by appointment over detected estrus, sixty-three multiparous animals were synchronized using a progestagen device inserted at day 31 and withdrawn after 9 days (average time for withdrawal was 40.9 ± 6.8 days). Cows were observed continuously from 0700 to 1800 during 72 h. The percentage of animals detected positive to estrus by riding behavior was 43.4%, transrectal palpation, 22% and heat detector patches, 4.6%. The percentage of cows ovulating as evidenced by progesterone samples taken at 9 and 11 days after the average estrus response was 73%. The economical assessment by using the values obtained in the present study afforded that the cost of semen, ovulation rate and fertility point to a definitive advantage of using AI at fixed time over a program based on estrus detection.展开更多
为了研究"精牧"系统检测奶牛发情效果,利用"精牧"奶牛独立发情检测系统检测试验所用的318头经产荷斯坦奶牛的发情数据,提取并保存,同时选派资深的发情检测人员对试验所用的318头牛只做直肠鉴定检测,并使用R语言(开...为了研究"精牧"系统检测奶牛发情效果,利用"精牧"奶牛独立发情检测系统检测试验所用的318头经产荷斯坦奶牛的发情数据,提取并保存,同时选派资深的发情检测人员对试验所用的318头牛只做直肠鉴定检测,并使用R语言(开源统计分析软件){gmodels}包中的CrossTable()函数和{caret}包中的confusionMatrix()函数,计算出"精牧"系统奶牛疑似发情检出率与正确率,利用混淆矩阵分析检验验证"精牧"奶牛独立发情检测系统检测数据与直肠鉴定检测数据的一致性。试验结果表明,"精牧"奶牛独立发情检测系统检测奶牛疑似发情检出率为0.957,正确率为0.903。Accuracy准确率为0.9465,Sensitivity(覆盖率或敏感度)值为0.9403,Specificity(负例覆盖率或特异度)值为0.9573,Pos Pred Value(命中率)值为0.9742和Neg Pred Value(负例命中率)值为0.9032,并且Kappa值为0.8865,充分说明"精牧"奶牛独立发情检测系统结果与直肠鉴定发情结果具有一致性。试验结果表明,"精牧"系统可以替代传统的鉴别方法鉴别奶牛是否发情。展开更多
基金This research activity described in this paper is supported in part by National Key Research and Development Program of China(Grant No.2018YFD0500705)National Natural Science Foundation of China(Grant No.61771184)Key Special Project in Intergovernmental International Scientific and Technological Innovation Cooperation of National Key Research and Development Program(Grant No.2019YFE0125600).
文摘The objective of this study was to assess the effectiveness and practicability of an activityindex combining acceleration and location data for automated estrus detection in dairycows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity recordsof each cow. The maximum-minimum distance clustering (MMDC) method was used todivide hourly activity data into low, medium, high, and intensity level groups. The weightedsum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation inhourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The studyshowed that the power consumption and communication effects of the neck tags wereacceptable for indoor-housing conditions. For the two consecutive time periods, theactivity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision,100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. Onthe basis of these results, it can be concluded that the combination of acceleration andlocation in the activity index can promote estrus detection in dairy cows.
基金This work was financially supported by the Tianjin Key Research and Development Program Science and Technology Support Key Project(Grant No.20YFZCSN00220)the Central Government Leading Local Science and Technology Development Special Project(Grant No.21ZYCGSN00590)the Inner Mongolia Autonomous Region Science and Technology Department Project(Grant No.2020GG0068).
文摘Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.
文摘To evaluate an artificial insemination program in postpartum Bos indicus cows and by using deterministic models, to estimate the possible economic advantages of insemination by appointment over detected estrus, sixty-three multiparous animals were synchronized using a progestagen device inserted at day 31 and withdrawn after 9 days (average time for withdrawal was 40.9 ± 6.8 days). Cows were observed continuously from 0700 to 1800 during 72 h. The percentage of animals detected positive to estrus by riding behavior was 43.4%, transrectal palpation, 22% and heat detector patches, 4.6%. The percentage of cows ovulating as evidenced by progesterone samples taken at 9 and 11 days after the average estrus response was 73%. The economical assessment by using the values obtained in the present study afforded that the cost of semen, ovulation rate and fertility point to a definitive advantage of using AI at fixed time over a program based on estrus detection.
文摘为了研究"精牧"系统检测奶牛发情效果,利用"精牧"奶牛独立发情检测系统检测试验所用的318头经产荷斯坦奶牛的发情数据,提取并保存,同时选派资深的发情检测人员对试验所用的318头牛只做直肠鉴定检测,并使用R语言(开源统计分析软件){gmodels}包中的CrossTable()函数和{caret}包中的confusionMatrix()函数,计算出"精牧"系统奶牛疑似发情检出率与正确率,利用混淆矩阵分析检验验证"精牧"奶牛独立发情检测系统检测数据与直肠鉴定检测数据的一致性。试验结果表明,"精牧"奶牛独立发情检测系统检测奶牛疑似发情检出率为0.957,正确率为0.903。Accuracy准确率为0.9465,Sensitivity(覆盖率或敏感度)值为0.9403,Specificity(负例覆盖率或特异度)值为0.9573,Pos Pred Value(命中率)值为0.9742和Neg Pred Value(负例命中率)值为0.9032,并且Kappa值为0.8865,充分说明"精牧"奶牛独立发情检测系统结果与直肠鉴定发情结果具有一致性。试验结果表明,"精牧"系统可以替代传统的鉴别方法鉴别奶牛是否发情。
