A Review of Livestock Smart Farming for Sustainable Food Security - Zaabar, Liyana Safra; Yacob, Adriana Arul; Nathan, Deventhren Kamala; Hing Yip, Emmerich Wong; Mat Razali, Noor Afiza (Society of Visual Informatics, 2025)
Maintaining food security via sustainable farming methods is a significant problem as the global population grows. This study aims to examine the impact of smart farming methods on enhancing farm animal output to satisfy rising demand while fostering sustainability. Smart livestock farming incorporates automation, Internet of Things (IoT) sensors, and machine learning algorithms to improve production, efficiency, and resource utilization. With an emphasis on essential factors including automated feeding, environmental monitoring, and health tracking, this study takes a methodical approach to reviewing IoT-based livestock farming. The efficiency of several sensor technologies, including motion, temperature, humidity, and biometric sensors, is examined in gathering data and making decisions in real time. The potential of machine learning methods like pattern identification, anomaly detection, and predictive analytics to maximize the production and health of farm animals is assessed. According to the results, IoT-driven livestock farming improves illness diagnosis, minimizes resource waste, and optimizes feeding practices, increasing production efficiency. These developments minimize the impact on the environment while promoting steady food production. Additionally, less human interference results from automation in livestock production, which lowers costs and improves decision-making. This study demonstrates how smart agricultural technology may be used to address issues related to food security. Further research is needed to increase real-time data processing, hone machine learning models, and investigate affordable options for broadly adopting these ideas into practice. Livestock management may be transformed, guaranteeing a robust and sustainable agricultural environment.