Assessment of Preference Behavior of Layer Hens under Different Light Colors and Temperature Environments in Long-Time Footage Using a Computer Vision System

被引:7
作者
Kodaira, Vanessa [1 ]
Siriani, Allan Lincoln Rodrigues [2 ]
Medeiros, Henry Ponti [3 ]
De Moura, Daniella Jorge [1 ]
Pereira, Danilo Florentino [1 ,4 ]
机构
[1] Univ Estadual Campinas, Fac Agr Engn, Grad Program Agr Engn, BR-13083875 Campinas, SP, Brazil
[2] Sao Paulo State Univ, Sch Sci & Engn, Grad Program Agribusiness & Dev, BR-17602496 Tupa, SP, Brazil
[3] Univ Florida, Dept Agr & Biol Engn, Gainesville, FL 32611 USA
[4] Sao Paulo State Univ, Sch Sci & Engn, Dept Management Dev & Technol, BR-17602496 Tupa, SP, Brazil
关键词
environmental stress; precision poultry farming; welfare; YOLO; MONOCHROMATIC LIGHT; EGG QUALITY; PRODUCTION PERFORMANCE; SPECTRAL SENSITIVITY; VISUAL PIGMENTS; LAYING HENS; RED-LIGHT; POULTRY; WELFARE; GROWTH;
D O I
10.3390/ani13152426
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Simple Summary Preference behavior can be an important indicator of animal welfare. The effects of different wavelengths of light on laying hens are not completely known. We performed a preference test with few laying hens with three different light sources (green, red, and white) in three thermal environmental conditions (cold, comfort and heat stress). We recorded video footage during the entire period of the experiment and automatically estimated the permanence of the birds in each room of the Environmental Preference Chamber using YOLO v4. The results showed that birds prefer white and red light under thermal comfort conditions and do not show preference under heat stress conditions. As for all birds, the behavior of chickens is largely determined by environmental conditions. In many production systems, light intensity is low and red feather strains have low contrast with the background, making it impossible to use conventional image segmentation techniques. On the other hand, studies of chicken behavior, even when using video camera resources, depend on human vision to extract the information of interest; and in this case, reduced samples are observed, due to the high cost of time and energy. Our work combined the use of advanced object detection techniques using YOLO v4 architecture to locate chickens in low-quality videos, and we automatically extracted information on the location of birds in more than 648 h of footage. We develop an automated system that allows the chickens to transition among three environments with different illuminations equipped with video cameras to monitor the presence of birds in each compartment, and we automatically count the number of birds in each compartment and determine their preference. Our chicken detection algorithm shows a mean average precision of 99.9%, and a manual inspection of the results showed an accuracy of 98.8%. Behavioral analysis results based on bird unrest index and permanence time indicate that chickens tend to prefer white light and disfavor green light, except in the presence of heat stress when no clear preference can be observed. This study demonstrates the potential of using computer vision techniques with low-resolution, low-cost cameras to monitor chickens in low-light conditions.
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页数:13
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