Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.

Journal: Sensors (Basel, Switzerland)
PMID:

Abstract

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.

Authors

  • Ebenezer Binuni Rebez
    Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India.
  • Veerasamy Sejian
    Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India.
  • Mullakkalparambil Velayudhan Silpa
    Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India.
  • Gajendirane Kalaignazhal
    Department of Animal Breeding and Genetics, College of Veterinary Science and Animal Husbandry, Odisha University of Agriculture and Technology, Bhubaneshwar 751003, India.
  • Duraisamy Thirunavukkarasu
    Department of Veterinary and Animal Husbandry Extension Education, Veterinary College and Research Institute, Tamil Nadu Veterinary and Animal Sciences University, Namakkal 637002, India.
  • Chinnasamy Devaraj
    ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India.
  • Kumar Tej Nikhil
    Rajiv Gandhi Institute of Veterinary Education and Research, Kurumbapet, Puducherry 605009, India.
  • Jacob Ninan
    Department of Nephrology and Critical Care Medicine, MultiCare Capital Medical Center, Olympia, WA, USA. jacob.ninan@multicare.org.
  • Artabandhu Sahoo
    ICAR-National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore 560030, India.
  • Nicola Lacetera
    Department of Agriculture and Forest Sciences, University of Tuscia, 01100 Viterbo, Italy.
  • Frank Rowland Dunshea
    School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, Melbourne, VIC 3010, Australia.