Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimen...Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass traits.However,because a pig is not constrained when an image is captured,the body does not always have a straight posture.This creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of uncertainty.The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig images.Firstly,the backbone line of a pig was extracted.Secondly,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments.Thirdly,the head and neck of the pig were removed from the pig’s contour by an ellipse.Finally,four length and one area parameters were calculated.The proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of pigs.Taking the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),respectively.These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.展开更多
基金This work was enclosed in the Flemish IWT funded project“Sustainable precision feeding”(Grant No.AIC-221.42.D.02),in collaboration with Agrifirm Innovation Center and Fancom.This work was also supported by the Fundamental Research Funds for the Central Universities of China(Grant No.KYZ201561)the Joint Innovation Fund of Production,Learning,and Research-Prospective Joint Research Project,Jiangsu,China(Grant No.BY2015071-06)the fund of China Scholarship Council(Grant No.201506855017).
文摘Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass traits.However,because a pig is not constrained when an image is captured,the body does not always have a straight posture.This creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of uncertainty.The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig images.Firstly,the backbone line of a pig was extracted.Secondly,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments.Thirdly,the head and neck of the pig were removed from the pig’s contour by an ellipse.Finally,four length and one area parameters were calculated.The proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of pigs.Taking the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),respectively.These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.
