A NOVEL METHOD FOR THE GROUP CHARACTERISTICS ANALYSIS OF YELLOW FEATHER BROILERS UNDER THE HEAT STRESS BASED ON OBJECT DETECTION AND TRANSFER LEARNING
Temperature is a very important factor in the breeding of yellow feather broilers. Researching the group behaviour of yellow feather broilers under heat stress can help farmers take the corresponding measures to reduce the heat stress of broilers and improve production performance. In this paper, several traditional methods have been employed to detect and locate the broilers. These methods are highly interfered by the background whose colour is similar to broilers, thereby making it difficult to accurately locate the broilers. Meanwhile, although the algorithm YOLOv3 can precisely segment and locate the broilers, it has the disadvantages of incomplete detection and low detection confidence rate. Finally, this paper applies the neural architecture search and transfer learning to train the pre processed training set, and obtains a detection model with a recognition accuracy of 83% Then this model is used to process the images under heat stress at every 30 s so as to obtain the distribution of the broilers at each moment. Based on the results, the liveness and distribution characteristics of the broilers are analyzed. The analysis results show that when heat stress occurs, the broilers mainly gather at the vent; that when the temperature further rises above 30degC, the proportion of broilers at the vent increases from 53.3% to 67.3% on average; and that the activity index of the broilers decreases by 22.54% within an average of 3 h after the temperature rises to 30degC. Zhai Yao Zai Huang Yu Ji Sheng Chan Yang Zhi Zhong ,Wen Du Shi Yi Ge Fei Chang Zhong Yao De Yin Su Yan Jiu Re Ying Ji Xia Huang Yu Ji De Qun Ti Xing Wei Ke Yi Bang Zhu Yang Zhi Hu Geng Shen Ru Di Liao Jie Huang Yu Ji Zai Re Ying Ji Xia De Fan Ying ,Cong Er Cai Qu Xiang Ying De Cuo Shi Jian Shao Ji De Re Ying Ji Xian Xiang ,Ti Gao Ji De Sheng Chan Xing Neng Ben Wen Shou Xian Yan Jiu Liao Ji Chong Dui Huang Yu Ji Jian Ce De Fang Fa ,Gen Ju Jian Ce De Jie Guo Wu Fa Jin Xing Xia Yi Bu De Jing Que Fen Xi ,Er Ji Yu Darknet 53 De YOLOv3 Suan Fa Ze Cun Zai Shi Bie Bu Jing Zhun De Wen Ti Zui Hou Ben Wen Li Yong Neural Architecture Search He Transfer Learning Dui Yu Chu Li Hou De Xun Lian Ji Jin Xing Xun Lian ,Huo De Liao Shi Bie Zhun Que Lu Wei 83%De Jian Ce Mo Xing Ji Yu Ci Mo Xing Fen Xi Ji Qun Huo Yue Du He Fen Bu Te Zheng Jie Guo Biao Ming ,Ji Qun Zai Wen Du Shang Sheng Zhi 30degCZhi Hou Ping Jun 3 Xiao Shi Nei Huo Yue Du Zhi Shu Hui Jiang Di 22.54% Zai Fen Bu Shang ,Re Ying Ji Fa Sheng Shi Ji Zhi Zhu Yao Ju Ji Zai Tong Feng Kou Chu ,Dang Wen Du Jin Yi Bu Sheng Gao Zhi 30degCYi Shang Shi ,Tong Feng Kou Chu Ji De Bi Li Ping Jun You 53.3%Shang Sheng Zhi 67.3% INTRODUCTION Heat stress is one of the most important environmental stressors challenging poultry production worldwide. Nowadays, broiler breeding industry has developed into large scale and specialized mode. As the number of broilers increases, the heat stress phenomenon of broilers will become more prominent in summer (Lara L.J. 2013) The physiological responses of broilers under heat stress divert energy from efficient production and increase the morbidity and mortality (Shakeri M. 2018) The negative effects of heat stress on yellow feather broilers include reduced growth and egg production, as well as decreased poultry and egg quality and safety (Goo D. 2019) Therefore, breeders must pay attention to the heat stress of broilers and take preventive measures. In most areas of China, especially southern China, the continuous high temperature and humid heat in summer easily bring heat stress to yellow feather broilers, which seriously affect the growth of broilers. Many researchers have investigated the effects of heat stress on poultry productivity and immune response. However, it is actually scarce on our knowledge of the basic mechanism related to the reported 1 Anlan Ding, Mr. Xuhui Zhang, Mr. Xiuguo Zou, Assoc. Prof. Ph.D. Yan Qian, Assoc. Prof. Ph.D. Heyang Yao, MAE. Stud. Shikai Zhang, MAE. Stud. Yuning Wei, MAE. Stud. Vol. 59, No. 3 2019 INMATEH 50 effects and the behaviour of poultry under heat stress. The analysis of the effects of heat stress on broilers mostly focuses on the physiological characteristics of broiler, but seldom on the group behaviour of the whole broiler flock (Habashy W.S. 2017; Huang S. 2018) The important information contained in animal behaviour is an intuitive index to evaluate animal welfare, which can enable producers to better manage livestock and poultry production (Dawkins M.S. 2012) Therefore, it is of great significance to investigate the behaviour of broilers under heat stress. In order to research the group behaviour of broilers, it is necessary to separate broilers from the background before further analysis. However, yellow feather broilers are raised in groups and have the same individual appearance, which makes it very difficult to detect and separate them individually. Moreover, the non rigid bodies of yellow feather broilers make it more difficult to research their behaviour than large animals' behaviour (Liu L. 2014) In recent years, some scholars are also trying to automatically identify the individual or group behaviours of laying hens or broilers through image processing technology (Aydin A. 2010; Leroy T. 2006) However, due to the unobvious contrast between livestock and poultry, and their backgrounds, background changes and uneven illumination, the extraction of broilers using traditional digital image processing technology would make errors, thus affecting the accuracy of analysis (Lao F. 2017) About the target extraction, a series of traditional algorithms (e.g. background difference method) and deep learning algorithms (e.g. RCNN, Fast RCNN, Faster RCNN and YOLO) have been proposed (Girshick R. 2014; Ren S. 2015; Redmon J. 2016) However, most of these algorithms are usually applied to the target extraction of pedestrians, vehicles and ships, etc. and rarely applied to the detection of small targets such as poultry (Wang D. 2018; Zhang H. 2019) In this paper, some traditional algorithms and deep learning algorithms are used to extract yellow feather broilers. Through the analysis and comparison of the extraction results, the final detection model is selected. Based on the accurate extraction of broilers by the detection model, the distribution index and activity of broilers are analyzed. At present, there is little research on calculating broiler distribution index and activity. Lao Tree Peony et al (Lao F. 2017) calculated the distribution index and activity of laying hens based on the pixels occupied by laying hens. In terms of distribution index, as the size of broiler is not always the same during the experiment, it is difficult to completely extract the pixels occupied by broilers, and the phenomenon of mutual overlap often occurs, which leads to large errors in calculating distribution index. Therefore, this paper proposes a method of using the number ratio of broiler as an index of distribution index based on the acquired images and the target detection algorithm. In terms of activity analysis, this paper makes some improvements to the original pixel method, where pixel variables are converted into area variables, and the activity is calculated based on the dislocation area of the regions occupied by yellow feather broilers in the previous image and in the next image. The experiment shows satisfactory results, which provides a reference for the prevention of heat stress of yellow feather broilers. MATERIALS AND METHODS Data Acquisition The broiler house used in this paper has two chambers designed by cotton coated coloured steel plates. Each broiler chamber has a width of 1.9 m, a length of 2.9 m, a height of 1.88 m on the western side and a height of 1.77 m on the eastern side. The roof is sloped to facilitate rainwater drainage. Inside the broiler chamber, we mounted a temperature and humidity monitoring system, a ventilation system and a video monitoring system. The floor is paved with wood panels and ventilation pipes are laid below (Zhang S. 2019; Yao H. 2018) The three dimensional chamber model was established by SolidWorks and is shown in Figure 1. Fig. 1 The three dimensional chamber model was established by SolidWorks Vol. 59, No. 3 2019 INMATEH 51 The data acquisition lasted from July 20th to August 8th, 2018. The cameras worked all day and the data were saved on the hard disk of the host computer. Broiler Target Detection Method Background Subtraction Algorithm The background subtraction algorithm is to subtract each current image from the background image stored in advance or acquired in real time, and calculate the area deviating from the background beyond a certain threshold value. Therefore, the target object is extracted. The specific operation flow is shown in Figure 2 (Zhao K. 2015) Nth frame image Differential image Threshold processing Distinguish background image minus Fig. 2 The specific operation flow of background subtraction algorithm Let the nth image and the background image be fn(x, y) and f(x, y) respectively, where x and y represent the coordinates of a pixel (x, y) The gray values of the corresponding pixels of the two frames of images are subtracted according to Equation (1) and their absolute values are used to obtain the differential image Dn (Braham M. 2016; Yang J. 2012) x,y f x,y fn x,y Dn (1) A threshold value T (manual test and selection based on the acquired image, and T 135 in this experiment) is set to binarize the image according to Equation (2) and a binary image Rn(x, y) is obtained. Among them, the point with a gray level of 255 is the front scenic spot, and the point with a gray level of 0 is the background point. The processing result is shown in Figure 3(c) else T y x Dn y x Rn 0 255 (2) (a) Background image (b) Pending image (c) Differential image Fig. 3 The processing result of background subtraction algorithm As shown in Figure 3, the extraction results by background subtraction algorithm are not satisfactory, mainly because the background environment is affected by illumination, camera shaking, and the variations of the background images at different moments. Moreover, the activities of yellow feather broilers change the positions of troughs and other objects in the background image, and their shadows increase the area of foreground, which reduces the extraction effect. Threshold Segmentation based on RGB Space RGB colour model is based on the principle of three primary colours of human vision and is the most basic colour model. It uses the three primary colours (red, green and blue) with different proport