Emerging Technologies and Artificial Intelligence in Agriculture

Abstract

The rapid development of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), remote sensing, and hyperautomation technologies is transforming modern agriculture into a highly efficient, data-driven ecosystem. This paper examines the integration of AI-driven irrigation systems, digital soil mapping, precision agriculture, and intelligent business process management platforms (iBMP) to optimize agricultural production, resource utilization, and environmental sustainability. The proposed approach combines sensor networks, satellite monitoring, unmanned aerial vehicles (UAVs), predictive analytics, and autonomous control systems to support real-time decision making, variable-rate irrigation, fertigation, crop monitoring, and disease detection. The study further explores the role of digital soil models, machine learning algorithms, and decision-support systems in improving water management, reducing environmental impact, and increasing crop productivity.