Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while com...Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while computer vision-based methods demand prolonged training periods and present challenges in implementation.To address these issues,this paper develops a novel precise cattle body detection solution,namely YOLOv5-VF-W3.By introducing the Varifocal loss,the YOLOv5-VF-W3 model can handle imbalanced samples and focus more attention on difficult-to-recognize instances.Additionally,the introduction of the WIoUv3 loss function provides the model with a wise gradient gain allocation strategy.This strategy reduces the competitiveness of high-quality anchor boxes while mitigating harmful gradients produced by low-quality anchor boxes,thereby emphasizing anchor boxes of ordinary quality.Through these enhancements,the YOLOv5-VF-W3 model can accurately detect cattle bodies,improving the efficiency and quality of animal husbandry production.Numerous experimental results have demonstrated that the proposed YOLOv5-VF-W3 model achieves superior cattle body detection results in both quantitative and qualitative evaluation criteria.Specifically,the YOLOv5-VF-W3 model achieves an mAP of 95.2%in cattle body detection,with individual cattle detection,leg detection,and head detection reaching 95.3%,94.8%,and 95.4%,respectively.Furthermore,in complex scenarios,especially when dealing with small targets and occlusions,the model can accurately and efficiently detect individual cattle and key body parts.This brings new opportunities for the development of precision livestock farming.展开更多
[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,publis...[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,published in Genbank. Polymerase chain reaction was used to analyze FⅪ deficiency of 576 Holstein calves in Henan,and the result was verified by DNA sequencing. [Result] We detect 576 cows,which include two carriers and one F Ⅺ deficiency,and the result was consistent with the DNA sequencing. The frequency of the FⅪ mutant allele was 0.3%,the carrier was 0.3%,the prevalence was 0.2%.[Conclusion]A method detecting FⅪ by polymerase chain reaction analysis was established. This method is not only simple and convenient,but also has a high accuracy and low cost,which is more suitable for large-scale FⅪ investigation.展开更多
Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain.The advanced technologies of machine learning and computer ...Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain.The advanced technologies of machine learning and computer vision have been applied in precision livestock management,including critical disease detection,vaccination,production management,tracking,and health monitoring.This paper offers a systematic literature review(SLR)of vision-based cattle identification.More specifically,this SLR is to identify and analyse the research related to cattle identification using Machine Learning(ML)and Deep Learning(DL).This study retrieved 731 studies from four online scholarly databases.Fifty-five articles were subsequently selected and investigated in depth.For the two main applications of cattle detection and cattle identification,all the ML based papers only solve cattle identification problems.However,both detection and identification problems were studied in the DL based papers.Based on our survey report,the most used ML models for cattle identification were support vector machine(SVM),k-nearest neighbour(KNN),and artificial neural network(ANN).Convolutional neural network(CNN),residual network(ResNet),Inception,You Only Look Once(YOLO),and Faster R-CNN were popular DL models in the selected papers.Among these papers,the most distinguishing features were the muzzle prints and coat patterns of cattle.Local binary pattern(LBP),speeded up robust features(SURF),scaleinvariant feature transform(SIFT),and Inception or CNN were identified as the most used feature extraction methods.This paper details important factors to consider when choosing a technique or method.We also identified major challenges in cattle identification.There are few publicly available datasets,and the quality of those datasets are affected by the wild environment and movement while collecting data.The processing time is a critical factor for a real-time cattle identification system.Finally,a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.展开更多
基金supported by the Shanxi Province Basic Research Program(Grant No.202203021212444)the GuangHe Fund D(Grant No.ghfund202407042032)+5 种基金Shanxi Agricultural University Science and Technology Innovation Enhancement Project(Grant No.CXGC2023045)Shanxi Postgraduate Education and Teaching Reform Project Fund(Grant No.2022YJJG094)Shanxi Agricultural University Doctoral Research Start-up Project(Grant No.2021BQ88)Shanxi Agricultural University Academic Restoration Research Project(Grant No.2020xshf38)Young and Middle-aged Top-notch Innovative Talent Cultivation Program of the Software College,Shanxi Agricultural University(Grant No.SXAUKY2024005)the Key Research and Development Program of Zhejiang Province under Grand(Grant No.2024C01104,2024001026).
文摘Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production.Traditional manual observation approaches are not only inefficient but also lack objectivity,while computer vision-based methods demand prolonged training periods and present challenges in implementation.To address these issues,this paper develops a novel precise cattle body detection solution,namely YOLOv5-VF-W3.By introducing the Varifocal loss,the YOLOv5-VF-W3 model can handle imbalanced samples and focus more attention on difficult-to-recognize instances.Additionally,the introduction of the WIoUv3 loss function provides the model with a wise gradient gain allocation strategy.This strategy reduces the competitiveness of high-quality anchor boxes while mitigating harmful gradients produced by low-quality anchor boxes,thereby emphasizing anchor boxes of ordinary quality.Through these enhancements,the YOLOv5-VF-W3 model can accurately detect cattle bodies,improving the efficiency and quality of animal husbandry production.Numerous experimental results have demonstrated that the proposed YOLOv5-VF-W3 model achieves superior cattle body detection results in both quantitative and qualitative evaluation criteria.Specifically,the YOLOv5-VF-W3 model achieves an mAP of 95.2%in cattle body detection,with individual cattle detection,leg detection,and head detection reaching 95.3%,94.8%,and 95.4%,respectively.Furthermore,in complex scenarios,especially when dealing with small targets and occlusions,the model can accurately and efficiently detect individual cattle and key body parts.This brings new opportunities for the development of precision livestock farming.
文摘[Objective] This study established a method to detect Factor Ⅺ by polymerase chain reaction analysis.[Method]A pair of primers was designed and synthesized according to sequences of FⅪ gene in Holstein calves,published in Genbank. Polymerase chain reaction was used to analyze FⅪ deficiency of 576 Holstein calves in Henan,and the result was verified by DNA sequencing. [Result] We detect 576 cows,which include two carriers and one F Ⅺ deficiency,and the result was consistent with the DNA sequencing. The frequency of the FⅪ mutant allele was 0.3%,the carrier was 0.3%,the prevalence was 0.2%.[Conclusion]A method detecting FⅪ by polymerase chain reaction analysis was established. This method is not only simple and convenient,but also has a high accuracy and low cost,which is more suitable for large-scale FⅪ investigation.
基金supported by funding from Food Agility CRC Ltd,funded under the Commonwealth Government CRC Program.
文摘Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain.The advanced technologies of machine learning and computer vision have been applied in precision livestock management,including critical disease detection,vaccination,production management,tracking,and health monitoring.This paper offers a systematic literature review(SLR)of vision-based cattle identification.More specifically,this SLR is to identify and analyse the research related to cattle identification using Machine Learning(ML)and Deep Learning(DL).This study retrieved 731 studies from four online scholarly databases.Fifty-five articles were subsequently selected and investigated in depth.For the two main applications of cattle detection and cattle identification,all the ML based papers only solve cattle identification problems.However,both detection and identification problems were studied in the DL based papers.Based on our survey report,the most used ML models for cattle identification were support vector machine(SVM),k-nearest neighbour(KNN),and artificial neural network(ANN).Convolutional neural network(CNN),residual network(ResNet),Inception,You Only Look Once(YOLO),and Faster R-CNN were popular DL models in the selected papers.Among these papers,the most distinguishing features were the muzzle prints and coat patterns of cattle.Local binary pattern(LBP),speeded up robust features(SURF),scaleinvariant feature transform(SIFT),and Inception or CNN were identified as the most used feature extraction methods.This paper details important factors to consider when choosing a technique or method.We also identified major challenges in cattle identification.There are few publicly available datasets,and the quality of those datasets are affected by the wild environment and movement while collecting data.The processing time is a critical factor for a real-time cattle identification system.Finally,a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.
