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
Cow identification is a prerequisite for precision livestock farming.Biometric-based methods have made significant progress in cow identification.However,substantial labelling costs and frequent identification task ch...Cow identification is a prerequisite for precision livestock farming.Biometric-based methods have made significant progress in cow identification.However,substantial labelling costs and frequent identification task changes are still hamper model application.In this work,a novel method called“MFCI”was proposed to achieve accurate cow identification under few-shot and task-changing conditions.Specifically,the proposed method comprises two components:cow location and cow identification.First,an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images.Then,the Model-Agnostic Meta-Learning(MAML)framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows.Moreover,an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches.The experimental results showed that the proposed cow location model achieved a mAP of 99.5%.The proposed cow identification model attained an accuracy of 90.43%with only five samples per cow for 20 cows,outperforming other state-of-the-art methods.The results demonstrate the broad applicability and significant value of the proposed method.展开更多
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.
基金supported by the National Key R&D Program of China(No.2023YFD1301800)the National Natural Science Foundation of China(No.32272931)+1 种基金the Shaanxi Province Agricultural Key Core Technology Project(No.2023NYGG005)the Shaanxi Provincial Technology Innovation Guidance Planned Program(No.2022QFY11-02).
文摘Cow identification is a prerequisite for precision livestock farming.Biometric-based methods have made significant progress in cow identification.However,substantial labelling costs and frequent identification task changes are still hamper model application.In this work,a novel method called“MFCI”was proposed to achieve accurate cow identification under few-shot and task-changing conditions.Specifically,the proposed method comprises two components:cow location and cow identification.First,an improved YOLOv5n with Ghost module was adopted to quickly detect cow locations in images.Then,the Model-Agnostic Meta-Learning(MAML)framework was introduced for accurate identification under few-shot conditions and for fast adaptation to frequent changes in individual cows.Moreover,an autoencoder was adopted to allow Base-Learner learn more generalized features by combining both supervised and unsupervised approaches.The experimental results showed that the proposed cow location model achieved a mAP of 99.5%.The proposed cow identification model attained an accuracy of 90.43%with only five samples per cow for 20 cows,outperforming other state-of-the-art methods.The results demonstrate the broad applicability and significant value of the proposed method.
