Data-Driven Decision Support in Smart Ubiquitous Agriculture

Abstract

Smart ubiquitous agriculture is a domain of industry that encompasses farming requiring profound monitoring, advanced technology, wireless data communication, and exceptional data analysis. The increasing interaction level with devices and automatic machines within agriculture creates new data challenges and requirements for decision support systems. My aim, within this paper, is to target the agricultural decision process using the Internet of Things (IoT), machine learning, and advanced predictive techniques. For this, I identify and examine the data acquisition methods, analytical tools, and decision-making devices that enable farmers and livestock managers to make the right choices and informed decisions on what actions to take. Using the selected intelligent systems, technologies, and automation techniques, I intend to demonstrate how these technologies can optimize the quantities and quality of output from agricultural undertakings while minimizing resource utilization. In addition, I highlight the unsolved problems of data redundancy, data protection, and manipulation, and dimensions of the issues associated with integrating information technology into smart farming, which need to be specified for decision-making within the farming environment. The studies and conclusions outlined in this paper prove that value-added information frameworks pave the road to agricultural planning and monitoring 2.0, which will enable food system production that is more responsive, exact, and environmentally sound.