Prof. Dr. Haikun Zheng
School of Mechanical and Energy Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, China
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Academic Career Length
6Years (2021-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2021 in Computers and Electronics in Agriculture
Journal Article
Cheng FangTZTiemin Zhang
Tiemin Zhang
Haikun ZhengJHJunduan Huang
Junduan Huang
KCKaixuan Cuan
Kaixuan Cuan
Journal Article
Haikun ZhengTZTiemin Zhang
Tiemin ZhangCheng FangJZJiayuan Zeng
Jiayuan ZengXYXiuli Yang
Xiuli YangAiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a management system application layer, (2) a data service layer, and (3) an information sensing layer. The information sensing layer obtains and uploads production and farming information through the wireless sensor network built in the poultry house. The use of a cloud database as an information storage carrier in the data service layer eliminates the complex status of deploying local server clusters, and it improves the flexibility and scalability of the system. The management system application layer contains many sub-function modules including poultry disease detection functions to realize the visual management of farming information and health farming; each module operates independently and cooperates with each other to form a set of information management system for poultry farming with wide functional coverage, high service efficiency, safety, and convenience. The system prototype has been tested for the performance of wireless sensor network and cloud database, and the results show that the prototype is capable of acquiring and managing poultry farming information.
Journal Article
Cheng FangHaikun ZhengJYJikang Yang
Jikang YangDFDeng Feng
Deng FengTZTiemin Zhang
Tiemin ZhangPoultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.
25 November 2022 in Computers and Electronics in Agriculture
Journal Article
23 April 2022 in Computers and Electronics in Agriculture
Journal Article
Haikun ZhengCheng FangTZTiemin Zhang
Tiemin Zhang
HZHongzhi Zhao
Hongzhi Zhao
JYJikang Yang
Jikang Yang
CMChuang Ma
Chuang Ma
Journal Article
Cheng Fang
Zhenlong WuHaikun ZhengJYJikang Yang
Jikang Yang
Chuang MaTZTiemin Zhang
Tiemin ZhangPoultry managers can better understand the state of poultry through poultry behavior analysis. As one of the key steps in behavior analysis, the accurate estimation of poultry posture is the focus of this research. This study mainly analyzes a top-down pose estimation method of multiple chickens. Therefore, we propose the “multi-chicken pose” (MCP), a pose estimation system for multiple chickens through deep learning. Firstly, we find the position of each chicken from the image via the chicken detector; then, an estimate of the pose of each chicken is made using a pose estimation network, which is based on transfer learning. On this basis, the pixel error (PE), root mean square error (RMSE), and image quantity distribution of key points are analyzed according to the improved chicken keypoint similarity (CKS). The experimental results show that the algorithm scores in different evaluation metrics are a mean average precision (mAP) of 0.652, a mean average recall (mAR) of 0.742, a percentage of correct keypoints (PCKs) of 0.789, and an RMSE of 17.30 pixels. To the best of our knowledge, this is the first time that transfer learning has been used for the pose estimation of multiple chickens as objects. The method can provide a new path for future poultry behavior analysis
About Me
Zheng Haikun, Ph.D. in Engineering, is a full-time faculty member at the School of Mechanical and Energy Engineering, Guangdong Ocean University. He earned his Ph.D. from the College of Engineering, South China Agricultural University. His primary research areas include computer vision detection technology, artificial intelligence applications, and smart agriculture and precision livestock farming. He has long been engaged in scientific and applied research focusing on automation and intelligent technology and equipment for production processes, the application of deep learning and neural network technology in interdisciplinary fields, and non-invasive measurement and detection methods for animal phenotypes and physiological states. He has published multiple SCI-indexed papers, and holds over twenty authorized invention patents and software copyrights.
As a state-sponsored joint Ph.D. candidate, he conducted visiting research at KU Leuven and has established long-term academic collaborations with scholars from institutions such as KU Leuven, RWTH Aachen University, and the University of Tennessee. He currently serves as a reviewer for academic journals including “Computers and Electronics in Agriculture”, “Agriculture”, “Animals”, “Scientific Reports”, etc.. He is also appointed as an "Expert in Artificial Intelligence" by Shaanxi Yukong Intelligent Technology Co., Ltd.