A real-time computer vision assessment and control of thermal comfort for group-housed pigs

A real-time image processing system was developed to detect movement and classify thermal comfort state of group-housed pigs based on their resting behavioral patterns. This paper describes the theory, system structure, selection and analysis of image features, and image classification techniques. Image moment invariants, run-length frequency, pig body occupation ratio, and pig group compactness are extracted as feature vectors. Minimum Euclidian distance was used to distinguish cold vs. comfortable state of the pigs; whereas blob analysis was used to identify warm/hot state of the pigs. A sliding window was employed to update reference image feature sets so that classification is always based on the most recent information. The prototype system was initially developed with paper-cut pigs, followed by tests with live pigs. The results showed that this system effectively detects animal movement, and correctly classifies animal thermal behaviors into cold, comfortable, or warm/hot conditions. It also has the ability to adopt itself to different body weight or sizes of the pigs.

[1]  Hongwei Xin,et al.  Swine responses to constant and modified diurnal cyclic temperatures , 1991 .

[2]  C. R. Boon,et al.  The effect of air speed changes on the group postural behaviour of pigs , 1982 .

[3]  W. H. Close,et al.  9 – THE CLIMATIC REQUIREMENTS OF THE PIG , 1981 .

[4]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[5]  G. L. Riskowski,et al.  Huddling Behavior and Hematology of Weanling Pigs as Affected by Air Velocity and Temperature , 1990 .

[6]  J Hu,et al.  Image-processing algorithms for behavior analysis of group-housed pigs , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[7]  Jay D. Harmon,et al.  Effects of fluctuating temperatures on isowean pigs. , 2000 .

[8]  L. Mount,et al.  The assessment of thermal environment in relation to pig production , 1975 .

[9]  L. Walford,et al.  Bioenergetics and Growth , 1947 .

[10]  Jay D. Harmon,et al.  Comparison of image feature extraction for classification of swine thermal comfort behavior , 1998 .

[11]  J. A. Nienaber,et al.  Air Temperature Influences on Swine Performance and Behavior , 1987 .

[12]  Rony Geers,et al.  ENVIRONMENTAL TEMPERATURE CONTROL BY THE PIG'S COMFORT BEHAVIOR THROUGH IMAGE ANALYSIS , 1991 .

[13]  K. Sällvik,et al.  The effects of air velocity and temperature on the behaviour and growth of pigs , 1984 .

[14]  Jay D. Harmon,et al.  Effects of Post-weaning Nutritional Conditions on Isowean Pigs , 1999 .

[15]  J. M. Bruce,et al.  Models of heat production and critical temperature for growing pigs , 1979 .

[16]  Hongwei Xin,et al.  THERMOGRAPHICAL QUANTIFICATION OF PHYSIOLOGICAL AND BEHAVIORAL RESPONSES OF GROUP-HOUSED YOUNG PIGS , 2000 .

[17]  Junqing Shao.  Classification of swine thermal comfort behavior by image processing and neural network , 1997 .

[18]  Yoram Yakimovsky,et al.  Boundary and Object Detection in Real World Images , 1974, JACM.

[19]  C. Boon.  The effect of departures from lower critical temperature on the group postural behaviour of pigs , 1981 .

[20]  Ramesh C. Jain,et al.  Illumination independent change detection for real world image sequences , 1989, Comput. Vis. Graph. Image Process..

[21]  Hongwei Xin,et al.  Application of machine vision to swine environmental control , 1997, Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics.