A review of three-dimensional computer vision used in precision livestock farming for cattle growth management

Within precision livestock farming, three-dimensional computer vision can improve growth monitoring in cattle management. To investigate the implementation of three-dimensional computer vision in cattle growth management, this systematic review, adhering to the PRISMA 2020 statement guideline, collected 47 eligible studies from the Web of Science database. Studies were analysed separately based on the incrementally encoded titles, and their outcomes were extracted and recorded in a pre-designed form. The survey of outcomes was conducted by using pivot analysis. The results showed that the body measurements assessment task contributed to other kinds of three-dimensional cattle growth tasks. Using Kinect sensors fixed at nadirs to obtain dorsal features was the most frequently applied approach in three-dimensional data acquisition. For three-dimensional data preprocessing, while empty scene subtraction was the most effective approach to removing background from point clouds, clustering and conditional filters were the most adopted functions to eliminate noise. In the discussion, this review provides actual insights into the knowledge of three-dimensional computer vision in cattle growth management, synthesises common considerations within data acquisition, forms a general procedure of data pre-processing, considers the potential of building an automatic and successive three-dimensional multi-task cattle growth monitoring management system, and discusses factors affecting the performance of models for cattle growth management. This review inspires the practice of future three-dimensional computer vision research in cattle growth management and could be extended to other livestock or wild animals.

Automated summary

Within precision livestock farming, three-dimensional computer vision is used to improve growth monitoring in cattle management. This systematic review investigates the implementation of three-dimensional computer vision in cattle growth management by collecting and analyzing 47 eligible studies. The results show that the body measurements assessment task contributes to other three-dimensional cattle growth tasks, and the most frequently applied approach for three-dimensional data acquisition is using Kinect sensors fixed at nadirs to obtain dorsal features. This review provides insights into three-dimensional computer vision in cattle growth management and discusses the potential of building an automatic and successive three-dimensional multi-task cattle growth monitoring management system.