BIG DATA BASED SMART SENSING FOR PRECISION AGRICULTURE USING ARTIFICIAL INTELLIGENCE - MOHAMMED RAFI, AWS I. ABUEID, MUHAMMAD UMAR, ACHRAF BEN MILED (Utilitas Mathematica, 2024)
By boosting production, efficiency, and sustainability, precision farming has revolutionized modern agricultural practices. Advances in big data and artificial intelligence (AI) make this feasible. This paper explores potential applications of AI and big data in conjunction with smart sensing technologies to enhance precision agriculture. In addition to outlining the fundamental components of smart sensing systems—data collection, processing, analysis, and decision-making—the article also demonstrates how these systems work to maximize resource usage, agricultural management, and environmental sustainability. Numerous AI approaches, such as machine learning and deep learning, are described in the context of their applications in the analysis of agricultural data collected from sensors. The paper also discusses the challenges and possible routes for developing and deploying big data-driven intelligent sensing systems for precision farming. This essay's main goals are to help readers understand the most current applications of big data in smart agriculture and the pertinent social and economic challenges that need to be addressed. This article covers big data applications that make sense for precision agriculture as well as data generating strategies, technology accessibility, device accessibility, software tool accessibility, and data analysis approaches. Furthermore, the widespread use of big data technologies in agriculture still faces several challenges.