A Systematic Literature Review on the Uses, Benefits, Challenges, and Prospects of Digital Twins in Livestock Farm Management
A Systematic Literature Review on the Uses, Benefits, Challenges, and Prospects of Digital Twins in Livestock Farm Management
Md Kamrul Hasan1,2, Hong-Seok Mun1,3, Keiven Mark Bigtasin Ampode1,4, Eddiemar Baguio Lagua1,5, Hae-Rang Park1,5, Young-Hwa Kim6, Md Sharifuzzaman1,7, Chul-Ju Yang1,5*
ABSTRACT
Digital twin (DT), an artificial intelligence (AI) technology, has garnered significant attention recently due to its potential applications across various industries, including the livestock sector. To facilitate the adoption and integration of DT technologies in livestock farming systems, this study aims to discern and appraise the most recent advancements by assessing scientific and technological progressions. The objective of this study is to conduct a comprehensive review of the literature to determine the status of DT application in livestock farm management with its benefits, challenges, and prospects. Scientific databases, PRISMA guidelines, and systematic bibliometric methodologies were used for searching and sorting the most fit articles. This study found that DT technology is new to the livestock industry and mainly used for environmental control, feed management, farm planning, greenhouse gas reduction, and behavior monitoring. Key barriers to DT deployment include high establishment costs, risk of market monopolization, and technical knowledge gaps. The amalgamation of government subsidies, localized technology production, and the provision of training facilities can be instrumental in enabling farmers to overcome the obstacles. The application of DT is limited in monitoring animal behavior and farm environment. However, DT technology has the potential for broader use, such as predicting growth, feed consumption, and improving the supply chain. Although these new areas are yet to be explored in real farm conditions. Along with expanding the new area of using DT in livestock farm management, future collaborative research should concentrate on creating new machine learning and/or deep learning models.
Keywords | Digital twin, Animal behavior monitoring, Environmental control, Farm operation, Livestock supply chain, Precision agriculture
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