Development of a temperature measurement system for a broiler flock with thermal imaging

作     者：Shen, Pao-Nan Lei, Perng-Kwei Liu, Ya-Cheng Haung, Ying-Jen Lin, Jeng-Liang 

作者机构：Department of Bio-industrial Mechatronics Engineering National Chung Hsing University 250 KuoKuang Road Taichung Taiwan Instrument Technology Research Center National Applied Research Laboratories 20 R&D Road VI Hsinchu Science Park Hsinchu Taiwan Department of Biomechatronic Engineering National Chiayi University 300 University Road Chiayi Taiwan 

出 版 物：《Engineering in Agriculture, Environment and Food》 (Eng. Agric. Environ. Food)

年 卷 期：2016年第9卷第3期

页      面：291-295页

核心收录：

基　　金：The authors are grateful to the Ministry of Science and Technology of Taiwan for financially supporting this research under Project MOST 103-2313-B-415 -004 

主　　题：Temperature measurement 

摘      要：Broiler body temperature is an important indicator of both a broiler s response to its environment and its overall health. This study used an infrared thermal camera to capture the thermal images of a broiler flock in a broiler house and then analyzed the temperatures of the broiler flock by image processing. Given the lack of feathers on a broiler s head and feet, the thermal images showed a raised temperature for these parts, making the head an appropriate part for measuring body temperature. The average or Gaussian filter was first used to smooth the images, after which the Otsu automatic threshold algorithm was used to determine the gray-scale thresholds and to segment the broiler s head and feet, followed by segmenting of the overlapping broiler images via the watershed method. Area filtering was then used to identify the broiler head blocks among the segmented blocks. Finally, the labeling methodology was used to calculate the number of broilers in each image. Analysis results show that the proposed image processing procedures can successfully identify the broiler head and analyze the temperatures of the broiler flock by capturing thermal images from a height of 160 cm and a depression angle of 30°, while achieving an identification accuracy of up to 91.3%. © 2016 Asian Agricultural and Biological Engineering Association