Next-Generation Smart Livestock Farming Through AI, Iot, And Environmental Intelligence

Abstract

Next-generation smart livestock farming is emerging as an important approach for improving animal health, welfare, productivity, resource efficiency, and environmental sustainability. This paper examines the integration of Artificial Intelligence (AI), Internet of Things (IoT), and environmental intelligence for developing intelligent and data-driven livestock management systems. IoT-enabled sensors, wearable devices, cameras, and environmental monitoring units enable continuous acquisition of animal physiological, behavioural, production, and surrounding environmental data. AI and machine learning techniques transform these heterogeneous data into useful information for disease risk prediction, behavioural assessment, environmental stress detection, welfare evaluation, and farm decision support. Environmental intelligence further strengthens the framework by analysing relationships between temperature, humidity, air quality, ventilation, climatic conditions, and animal responses. The proposed integrated framework combines animal and environmental sensing, IoT communication, edge and cloud processing, multi-source data integration, AI-based prediction, risk assessment, and intelligent decision support. It supports proactive management through real-time monitoring, early alerts, predictive analytics, and adaptive environmental control. The study also highlights challenges involving data quality, sensor reliability, interoperability, cybersecurity, model explainability, and generalizability. Overall, integrating AI, IoT, and environmental intelligence provides a promising pathway toward predictive, adaptive, sustainable, and welfare-oriented livestock farming.