Automatic method for quantitatively analyzing the body condition of livestock from 3D shape

Zhang, Jialong; Lei, Jie; Wu, Jianhuan; Lu, Hexiao; Guo, Hao; Pezzuolo, Andrea; Kolpakov, Vladimir; Ruchay, Alexey

doi:10.1016/j.compag.2023.108307

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The analysis of feed consumption, as a crucial indicator of production performance and the risk of diseases in dairy cows [ 12 ], and the measurement of animal dimensions as essential indicators to monitor growth rate, food efficiency, and health status [ 13 ], highlight the importance of assessing body condition as an indicator of cattle health. Advances in the quantitative analysis of three-dimensional shapes [ 14 ] promote the development of smart agriculture.…”

Section: Introductionmentioning

confidence: 99%

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis

de Oliveira,

Ferraz,

André

et al. 2024

Animals

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The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are “Computers and Electronics in Agriculture” and “Journal of Dairy Science”. It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

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Section: Introductionmentioning

confidence: 99%