Role of computer vision and deep learning algorithms in livestock behavioural recognition: A state-of-the-art- review

Olawuyi Fatoki (), Chunling Tu (), Robert Hans () and Rotimi-Williams Bello ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 6416-6430

Abstract: The increasing demand for sustainable livestock products necessitates a re-evaluation of animal production and breeding practices. Contemporary breeding programs now integrate animal phenotypic behaviors due to their considerable influence on productivity, health, and welfare, which ultimately impact industry yield and economic outcomes. Monitoring animal behavior manually is challenging and subjective, especially in continuous or large-scale operations, as it is time-consuming and labor-intensive. Consequently, computer vision technology has attracted attention for its objectivity, non-invasiveness, and capacity for continuous monitoring. However, recognizing livestock behavior using computer vision remains difficult due to complex scenes and varying conditions, hindering its widespread adoption in the industry. Deep learning technology has emerged as a promising solution, mitigating some of these challenges and enhancing the recognition of livestock behaviors. This paper reviews recent advancements in computer vision methods for detecting behaviors in livestock such as cattle with an emphasis on behaviors critical for health, welfare, and productivity. It investigates the development of both traditional computer vision and deep learning techniques for image segmentation, identification, and behavior recognition. The review explores the development of research trends in livestock behavior recognition, focusing on improvements in reliable identification algorithms, the analysis of behaviors at different growth stages, the measurement of behavioral data, and the design of systems to evaluate welfare, health, growth, and development.

Keywords: Behaviour; Cattle; Computer vision; Deep learning; Livestock. (search for similar items in EconPapers)
Date: 2024
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