Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

Livestock’s live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.