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Arshad MF, Burrai GP, Varcasia A, Sini MF, Ahmed F, Lai G, Polinas M, Antuofermo E, Tamponi C, Cocco R, Corda A, Parpaglia MLP. The groundbreaking impact of digitalization and artificial intelligence in sheep farming. Res Vet Sci 2024; 170:105197. [PMID: 38395008 DOI: 10.1016/j.rvsc.2024.105197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/12/2024] [Accepted: 02/19/2024] [Indexed: 02/25/2024]
Abstract
The integration of digitalization and Artificial Intelligence (AI) has marked the onset of a new era of efficient sheep farming in multiple aspects ranging from the general well-being of sheep to advanced web-based management applications. The resultant improvement in sheep health and consequently better farming yield has already started to benefit both farmers and veterinarians. The predictive analytical models embedded with machine learning (giving sense to machines) has helped better decision-making and has enabled farmers to derive most out of their farms. This is evident in the ability of farmers to remotely monitor livestock health by wearable devices that keep track of animal vital signs and behaviour. Additionally, veterinarians now employ advanced AI-based diagnostics for efficient parasite detection and control. Overall, digitalization and AI have completely transformed traditional farming practices in livestock animals. However, there is a pressing need to optimize digital sheep farming, allowing sheep farmers to appreciate and adopt these innovative systems. To fill this gap, this review aims to provide available digital and AI-based systems designed to aid precision farming of sheep, offering an up-to-date understanding on the subject. Various contemporary techniques, such as sky shepherding, virtual fencing, advanced parasite detection, automated counting and behaviour tracking, anomaly detection, precision nutrition, breeding support, and several mobile-based management applications are currently being utilized in sheep farms and appear to be promising. Although artificial intelligence and machine learning may represent key features in the sustainable development of sheep farming, they present numerous challenges in application.
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Affiliation(s)
| | | | - Antonio Varcasia
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
| | | | - Fahad Ahmed
- Nutrition Innovation Centre for Food and Health (NICHE), School of Biomedical Sciences, Ulster University, Coleraine BT52 1SA, UK
| | - Giovanni Lai
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Marta Polinas
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | | | - Claudia Tamponi
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Raffaella Cocco
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Andrea Corda
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
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