Artificial Intelligence and Digital Technologies for Sustainable Animal Environment Management: Innovations, Challenges, and Future Perspective

Abstract

The mounting demands of climate change, environmental degradation, scarcity of resources and rising animal products demand have necessitated more environmentally and sustainably friendly methods of animal environment management. Considering the role of Artificial Intelligence (AI) and digital technologies to enhance animal welfare, environmental monitoring, resource efficiency, and sustainable decision-making, this paper explores the topic. The researcher has a secondary-data-based computational design, where data processing, exploratory analysis, and machine-learning fulfill data processing roles as part of the study. Such technologies as Internet of Things (IoT) sensors, computer vision, predictive analytics, cloud computing, robotics, and smart sensing systems are discussed in the context of their use in the animal and agrarian fields. Analysis shows that constant protocols of temperature, humidity, air quality, animal activities, feed intake, and water intake can facilitate the prompt identification of changes in the environment and behaviour. Machine-learning methods with a particularly high likelihood of classifying environmental risks and enabling predictive management are the ensemble-based approaches. Other issues, though, such as implementation costs and quality of data, cybersecurity, infrastructure constraints, technical expertise, ethical issues, and scalability also are major obstacles. In its concluding part, the paper suggests that a transition to the reactive to the predictive animal environment management is possible by implementing AI with the use of digital sensing and intelligent decision-support systems. The further evolution must focus on affordable technologies, the explainable AI, ethical governance, and field-based validation to deliver sustainable and welfare-oriented results.