Modern pig farming leaves much to be desired in terms of efficiency,as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight.This method is not only inefficient...Modern pig farming leaves much to be desired in terms of efficiency,as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight.This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases.Furthermore,confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs.This research executed an experiment to construct a deep-learning sorting mechanism,leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months.This research integrated a Kalman filterbased algorithm to augment the precision of the dynamic sorting operation.This research experiment unveiled a pioneering machine vision sorting system powered by deep learning,adept at handling live imagery for multifaceted recognition objectives.The Individual recognition model based on Residual Neural Network(ResNet)monitors livestock weight for sustained data forecasting,whereas the Wasserstein Generative Adversarial Nets(WGAN)image enhancement algorithm bolsters recognition in distinct settings,fortifying the model's resilience.Notably,system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety.Experimental outcomes validate the superiority of this proposed system over traditional counterparts.It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage.This findings reflect an 89%success rate in livestock ID recognition,a 32%surge in obscured image recognition,a 95%leap in livestock categorization accuracy,and a remarkable 98%success rate in discerning images of unwell pigs.In essence,this research augments identification efficiency,curtails operational expenses,and provides enhanced tools for disease monitoring.展开更多
文摘Modern pig farming leaves much to be desired in terms of efficiency,as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight.This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases.Furthermore,confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs.This research executed an experiment to construct a deep-learning sorting mechanism,leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months.This research integrated a Kalman filterbased algorithm to augment the precision of the dynamic sorting operation.This research experiment unveiled a pioneering machine vision sorting system powered by deep learning,adept at handling live imagery for multifaceted recognition objectives.The Individual recognition model based on Residual Neural Network(ResNet)monitors livestock weight for sustained data forecasting,whereas the Wasserstein Generative Adversarial Nets(WGAN)image enhancement algorithm bolsters recognition in distinct settings,fortifying the model's resilience.Notably,system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety.Experimental outcomes validate the superiority of this proposed system over traditional counterparts.It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage.This findings reflect an 89%success rate in livestock ID recognition,a 32%surge in obscured image recognition,a 95%leap in livestock categorization accuracy,and a remarkable 98%success rate in discerning images of unwell pigs.In essence,this research augments identification efficiency,curtails operational expenses,and provides enhanced tools for disease monitoring.
