Your browser doesn't support javascript.
loading
The groundbreaking impact of digitalization and artificial intelligence in sheep farming.
Arshad, Muhammad Furqan; Burrai, Giovanni Pietro; Varcasia, Antonio; Sini, Maria Francesca; Ahmed, Fahad; Lai, Giovanni; Polinas, Marta; Antuofermo, Elisabetta; Tamponi, Claudia; Cocco, Raffaella; Corda, Andrea; Parpaglia, Maria Luisa Pinna.
Afiliación
  • Arshad MF; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Burrai GP; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Varcasia A; Department of Veterinary Medicine, University of Sassari, Sassari, Italy. Electronic address: varcasia@uniss.it.
  • Sini MF; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Ahmed F; Nutrition Innovation Centre for Food and Health (NICHE), School of Biomedical Sciences, Ulster University, Coleraine BT52 1SA, UK.
  • Lai G; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Polinas M; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Antuofermo E; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Tamponi C; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Cocco R; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Corda A; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
  • Parpaglia MLP; Department of Veterinary Medicine, University of Sassari, Sassari, Italy.
Res Vet Sci ; 170: 105197, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38395008
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.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Crianza de Animales Domésticos Límite: Animals / Humans Idioma: En Revista: Res Vet Sci Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Crianza de Animales Domésticos Límite: Animals / Humans Idioma: En Revista: Res Vet Sci Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido