Calf Posture Recognition Using Convolutional Neural Network

作     者：Tan Chen Tung Uswah Khairuddin Mohd Ibrahim Shapiai Norhariani Md Nor Mark Wen Han Hiew Nurul Aisyah Mohd Suhaimie 

作者机构：Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Veterinary MedicineUniversiti Putra MalaysiaSelangor43400Malaysia Faculty of Bioresources and Food IndustryBesut22200Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第1期

页      面：1493-1508页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded under the Malaysian Young Researchers grant scheme(MRUN-MYRGS)Vote number:5539500(Universiti Putra Malaysia)Title:Precision surveillance system to support dairy young stock rearing decisions(NMN) 

主　　题：Calf posture machine vision deep learning transfer learning 

摘      要：Dairy farm management is crucial to maintain the longevity of the farm,and poor dairy youngstock or calf management could lead to gradually deteriorating calf health,which often causes premature *** was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent *** posture recognition is one of the effective methods to monitor calf behaviour and health state,which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf,and the latter,passive *** posture recognition module is an important component of some automated calf monitoring systems,as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring,or at the very least,to monitor the activeness of the *** posture such as standing or resting can easily be distinguished by human eye,however,to be recognized by a machine,it will require more complicated frameworks,particularly one that involves a deep learning neural networks *** number of highquality images are required to train a deep learning model for such *** this paper,multiple ConvolutionalNeuralNetwork(CNN)architectures were compared,and the residual network(ResNet)model(specifically,ResNet-50)was ultimately chosen due to its simplicity,great performance,and decent inference *** ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different *** were two camera placements to use for comparison because camera placements can significantly impact the quality of the images,which is highly correlated to the deep learning model *** model training,the performance for both CNN models were 99.7%and 99.99%accuracies,respectively,and is adequate for a real-time calf monitoring system.