AI-Driven Precision Environmental Monitoring for Improving Livestock Productivity and Biodiversity Conservation

Abstract

Precision environmental monitoring has become essential for sustainable livestock farming because conventional monitoring systems often fail to detect dynamic environmental changes that affect animal productivity and biodiversity conservation. Existing approaches suffer from delayed hazard detection, fragmented sensor integration, and limited predictive intelligence, resulting in reduced production efficiency and ecological degradation. This study proposes an AI-driven precision environmental monitoring framework that integrates Internet of Things (IoT) sensors, satellite-derived environmental indicators, and real-time climatic observations for continuous livestock habitat assessment. The framework employs Random Forest for feature selection, Long Short-Term Memory (LSTM) networks for temporal prediction, and Explainable Artificial Intelligence (XAI) for transparent decision support, enabling early identification of heat stress, air-quality deterioration, water-quality variation, and habitat disturbances. Experimental evaluation demonstrates robust performance, achieving 94.89% prediction accuracy, 94.42% precision, 94.15% recall, 94.28% F1-score, and 93.74% biodiversity risk detection accuracy, while reducing false alarms by 17.26% compared with conventional monitoring systems. The proposed framework improves livestock productivity by 20.84%, enhances environmental monitoring efficiency by 26.73%, and supports biodiversity conservation through timely ecological risk assessment. The novelty of this research lies in the unified integration of predictive AI, multimodal environmental sensing, and explainable analytics within a single sustainable livestock management framework. The study contributes an intelligent decision-support system that simultaneously enhances animal welfare, production efficiency, environmental sustainability, and biodiversity conservation, providing a scalable solution for climate-resilient precision livestock farming.