# A Review of Posture Detection Methods for Pigs Using Deep Learning
###### Abstract  
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, stemal 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. pig posture detection; deep learning; machine vision +
Footnote †: journal: 2023, 13, 6997  
However, in previous research on animal behavior and posture, pig posture and location were typically recorded through on-the-spot observation or monitoring records [13]. Similarly, in real production environments, experts or breeders performed these procedures to adjust breeding strategies accordingly [14]. However, manual pig posture detection is considerably time-consuming therefore leading to low efficiency in the assessment of pig welfare. Moreover, even sophisticated experts may have the problems such as subjectivity, inconsistency, and even incorrect differentiation between highly similar postures, such as sternal recumbency and ventral recumbency [15]. Therefore, manual detection of pig posture is not feasible for commercial farm construction. With the rapid development of artificial intelligence (AI) in recent years, the concept of smart farming has gradually gained popularity in pig farming for automatic and continuous monitoring of livestock's behavioral patterns, such as pig posture [16; 17]. Naturally, highly efficient and effective methods for pig posture detection are crucial for such smart farming construction [18]. Fortunately, various animal-attached sensors and cameras have been introduced into the research scenarios and practical pig farms, enabling the capture and monitoring of animal biomarkers, such as pig posture [19]. With the assistance of these types of equipment, the progressive development of automated posture detection methods has been contributing to satisfactory posture detection schemes and stimulating smart farming construction [20]. With the evolution from complex sensors to common cameras, from early manual modeling to traditional machine vision, and then to deep learning, a multitude of detection methods have been proposed. Among them, detection methods based on deep learning have shown superior performance and promising potential to be popularized in commercial production on a large scale. This review will comprehensively introduce the data acquisition process and sub-tasks for pig posture detection as well as their technological evolutionary processes. Additionally, it will also summarize various advanced deep learning methods applied in pig posture detection, with a primary focus on camera-assisted techniques.