Artificial Intelligence Analysis of Farm Noise Effects on Livestock Welfare

Abstract

Introduction: A frequently ignored stressor for farm animals is that of noise caused by farm machinery, ventilation system, transportation and human actions. High or sudden noise has been linked to high levels of cortisol, low feed intake, disturbed rest patterns, and lower production rates in milk or any livestock. The current welfare measurement techniques for the animals depend on manual observations that are subjective, tedious and hard to be scaled up. This research focuses on the use of artificial intelligence (AI) to understand the effects of noise on the welfare of farm animals. Through the integration of acoustic sensors, machine learning techniques, computer vision, and biosensors in animals, it is possible to monitor the effects of noise on animal behavior and physiology on an ongoing basis. Sound classification, activity monitoring, and risk prediction are among the methods used to detect stress in animals. The results show that using AI will help manage animal welfare proactively, make informed designs on facilities, and make more objective decisions. However, challenges such as expensive sensors, differences between breeds and species, and the need for a large labeled dataset impede the broad use of AI. In summary, using artificial intelligence to analyze noise is a promising approach to enhance animal welfare and farming.