数字农科院2.0

A Review of Posture Detection Methods for Pigs Using Deep Learning

文献类型： 外文期刊

作者： Chen, Zhe;Lu, Jisheng;Wang, Haiyan

作者机构：

关键词： pig posture detection;deep learning;machine vision

期刊名称： APPLIED SCIENCES-BASEL

ISSN：

年卷期： 2023 年 13 卷 12 期

页码：

收录情况： SCIE（2023版）

摘要： Analysis of pig posture is significant for improving the welfare and yield of captive pigs under different conditions. Detection of pig postures, such as standing, lateral lying, sternal lying, and sitting, can facilitate a comprehensive assessment of the psychological and physiological conditions of pigs, prediction of their abnormal or detrimental behavior, and evaluation of the farming conditions to improve pig welfare and yield. With the introduction of smart farming into the farming industry, effective and applicable posture detection methods become indispensable for realizing the above purposes in an intelligent and automatic manner. From early manual modeling to traditional machine vision, and then to deep learning, multifarious detection methods have been proposed to meet the practical demand. Posture detection methods based on deep learning show great superiority in terms of performance (such as accuracy, speed, and robustness) and feasibility (such as simplicity and universality) compared with most traditional methods. It is promising to popularize deep learning technology in actual commercial production on a large scale to automate pig posture monitoring. This review comprehensively introduces the data acquisition methods and sub-tasks for pig posture detection and their technological evolutionary processes, and also summarizes the application of mainstream deep learning models in pig posture detection. Finally, the limitations of current methods and the future directions for research will be discussed.

分类号：

  • 相关文献

[1]YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs. Wenhui Shi,Xiaopin Wang,Xuan Li,Yuhua Fu,Xiaolei Liu,Haiyan Wang. 2025

[2]Bionic technology and deep learning in agricultural engineering: Current status and future prospects. Chunlei Tu,Jinxia Li,Xingsong Wang,Shen Cheng,Jie Li. 2021

[3]Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision. Sun, Guojia;Liu, Shuhua;Luo, Haolun;Feng, Zelin;Yang, Baojun;Luo, Ju;Tang, Jian,Yao, Qing,Xu, Jiajun. 2022

[4]Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. Qiang, Lu,Lie, Deng,Cai Jianrong,Bin, Liu,Zhang Yajing. 2014

[5]Plant Identification Based on Artificial Intelligence. Hou Chun Sheng,Zhou Ting. 2011

[6]Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Zhongyuan Liu,Rentian Zhang,Chongshan Yang,Bin Hu,Xin Luo,Yang Li,Chunwang Dong. 2022

[7]Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision. Sheng, Xufeng,Zan, Jiezhong,Jiang, Yongwen,Shen, Shuai,Li, Li,Yuan, Haibo. 2023

[8]Application status and challenges of machine vision in plant factory—A review. Zhiwei Tian,Wei Ma,Qichang Yang,Famin Duan. 2022

[9]The Application Status and Trends of Machine Vision in Tea Production. Zhiming Yang,Wei Ma,Jinzhu Lu,Zhiwei Tian,Kaiqian Peng. 2023

[10]Application of Machine Vision Technology in Citrus Production. Kaiqian Peng,Wei Ma,Jinzhu Lu,Zhiwei Tian,Zhiming Yang. 2023

[11]DESIGN AND IMPLEMENTATION OF THE POSITIONING AND DIRECTING PRECISION SEEDER FOR CUCURBITA FICIFOLIA SEEDS. Ding, Xinting,Chen, Tong,Wei, Yongzhe,He, Zhi,Cui, Yongjie,Yang, Qichang. 2024

[12]From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods. Junhua Lu,Mei Zhang,Yongsong Hu,Wei Ma,Zhiwei Tian,Hongsen Liao,Jiawei Chen,Yuxin Yang. 2024

[13]Rep-ViG-Apple: A CNN-GCN Hybrid Model for Apple Detection in Complex Orchard Environments. Bo Han,Ziao Lu,Jingjing Zhang,Rolla Almodfer,Zhengting Wang,Wei Sun,Luan Dong. 2024

[14]A Machine Vision-Based Method of Impurity Detection for Rapeseed Harvesters. Xu Chen,Zhuohuai Guan,Haitong Li,Min Zhang. 2024

[15]The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s. Hao Ma,Yulong Ding,Hongwei Cui,Jiangtao Ji,Xin Jin,Tianhang Ding,Jiaoling Wang. 2025

[16]Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology. Hanting Zou,Tianmeng Lan,Yongwen Jiang,Xiao Lan Yu,Haibo Yuan. 2024

[17]Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques. Bailin Yue,Yong Jin,Shangrong Wu,Jieyang Tan,Youxing Chen,Hu Zhong,Guipeng Chen,Yingbin Deng. 2025

[18]Rapid and Quantitative Prediction of Tea Pigments Content During the Rolling of Black Tea by Multi-Source Information Fusion and System Analysis Methods. Zou, Hanting,Li, Ranyang,Xuan, Xuan,Jiang, Yongwen,Yuan, Haibo,An, Ting. 2025

[19]Intelligent Batch Harvesting of Trellis-Grown Fruits with Application to Kiwifruit Picking Robots. Yuxin Yang,Mei Zhang,Wei Ma,Yongsong Hu. 2025

[20]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

作者其他论文 更多>>