The paternity index is one of the important parameters which paternity determination depends on.Inbreeding is an indispensable and effective means to improve herds and breeds and breed new strains and breeds.It can fi...The paternity index is one of the important parameters which paternity determination depends on.Inbreeding is an indispensable and effective means to improve herds and breeds and breed new strains and breeds.It can fix good traits and improve herd genetic uniformity.The INBREED module of SAS statistical analysis software can be used to calculate the inbreeding coefficients of the offspring and their parents in the pig herd pedigree.In this study,we used actual data as an example to compile and operate an SAS program for calculating the inbreeding coefficients of a pig herd.Compared with the dedicated software for calculating inbreeding coefficients developed in recent years,such as BASIC+database dBASE,Visual Basic+database SQ L Serve method,DFREMLI,MTDF EMLI,VCE,ASREML,DMU,GBS and Herdsman,calculating inbreeding coefficients with SAS programs has the advantages of low cost,simple programming language,and easy operation.For livestock breeders who are not provided with special computing software,the use of SAS to calculate the inbreeding coefficients of pigs is of great significance to planned breed selection and assortative mating.展开更多
群养猪行为是评估猪群对环境适应性的重要指标。猪场环境中,猪只行为识别易受不同光线和猪只粘连等因素影响,为提高群养猪只行为识别精度与效率,该研究提出一种基于改进帧间差分-深度学习的群养猪只饮食、躺卧、站立和打斗等典型行为识...群养猪行为是评估猪群对环境适应性的重要指标。猪场环境中,猪只行为识别易受不同光线和猪只粘连等因素影响,为提高群养猪只行为识别精度与效率,该研究提出一种基于改进帧间差分-深度学习的群养猪只饮食、躺卧、站立和打斗等典型行为识别方法。该研究以18只50~115日龄长白猪为研究对象,采集视频帧1117张,经图像增强共得到4468张图像作为数据集。首先,选取Faster R-CNN、SSD、Retinanet、Detection Transformer和YOLOv5五种典型深度学习模型进行姿态检测研究,通过对比分析,确定了最优姿态检测模型;然后,对传统帧间差分法进行了改进,改进后帧间差分法能有效提取猪只完整的活动像素特征,使检测结果接近实际运动猪只目标;最后,引入打斗活动比例(Proportion of Fighting Activities,PFA)和打斗行为比例(Proportion of Fighting Behavior,PFB)2个指标优化猪只打斗行为识别模型,并对模型进行评价,确定最优行为模型。经测试,YOLOv5对群养猪只典型姿态检测平均精度均值达93.80%,模型大小为14.40MB,检测速度为32.00帧/s,检测速度满足姿态实时检测需求,与FasterR-CNN、SSD、Retinanet和DetectionTransformer模型相比,YOLOv5平均精度均值分别提高了1.10、3.23、4.15和21.20个百分点,模型大小分别减小了87.31%、85.09%、90.15%和97.10%。同时,当两个优化指标PFA和PFB分别设置为10%和40%时,猪只典型行为识别结果最佳,识别准确率均值为94.45%。结果表明,该方法具有准确率高、模型小和识别速度快等优点。该研究为群养猪只典型行为精准高效识别提供方法参考。展开更多
文摘The paternity index is one of the important parameters which paternity determination depends on.Inbreeding is an indispensable and effective means to improve herds and breeds and breed new strains and breeds.It can fix good traits and improve herd genetic uniformity.The INBREED module of SAS statistical analysis software can be used to calculate the inbreeding coefficients of the offspring and their parents in the pig herd pedigree.In this study,we used actual data as an example to compile and operate an SAS program for calculating the inbreeding coefficients of a pig herd.Compared with the dedicated software for calculating inbreeding coefficients developed in recent years,such as BASIC+database dBASE,Visual Basic+database SQ L Serve method,DFREMLI,MTDF EMLI,VCE,ASREML,DMU,GBS and Herdsman,calculating inbreeding coefficients with SAS programs has the advantages of low cost,simple programming language,and easy operation.For livestock breeders who are not provided with special computing software,the use of SAS to calculate the inbreeding coefficients of pigs is of great significance to planned breed selection and assortative mating.
文摘群养猪行为是评估猪群对环境适应性的重要指标。猪场环境中,猪只行为识别易受不同光线和猪只粘连等因素影响,为提高群养猪只行为识别精度与效率,该研究提出一种基于改进帧间差分-深度学习的群养猪只饮食、躺卧、站立和打斗等典型行为识别方法。该研究以18只50~115日龄长白猪为研究对象,采集视频帧1117张,经图像增强共得到4468张图像作为数据集。首先,选取Faster R-CNN、SSD、Retinanet、Detection Transformer和YOLOv5五种典型深度学习模型进行姿态检测研究,通过对比分析,确定了最优姿态检测模型;然后,对传统帧间差分法进行了改进,改进后帧间差分法能有效提取猪只完整的活动像素特征,使检测结果接近实际运动猪只目标;最后,引入打斗活动比例(Proportion of Fighting Activities,PFA)和打斗行为比例(Proportion of Fighting Behavior,PFB)2个指标优化猪只打斗行为识别模型,并对模型进行评价,确定最优行为模型。经测试,YOLOv5对群养猪只典型姿态检测平均精度均值达93.80%,模型大小为14.40MB,检测速度为32.00帧/s,检测速度满足姿态实时检测需求,与FasterR-CNN、SSD、Retinanet和DetectionTransformer模型相比,YOLOv5平均精度均值分别提高了1.10、3.23、4.15和21.20个百分点,模型大小分别减小了87.31%、85.09%、90.15%和97.10%。同时,当两个优化指标PFA和PFB分别设置为10%和40%时,猪只典型行为识别结果最佳,识别准确率均值为94.45%。结果表明,该方法具有准确率高、模型小和识别速度快等优点。该研究为群养猪只典型行为精准高效识别提供方法参考。
