A Machine-People-Government Triangular Model of Smart Agriculture

Artificial intelligence (AI) is driving the transformation and upgrading of traditional agriculture towards digitization and intelligence, improving agricultural efficiency and structural optimization. The agricultural environment is dynamic, with numerous factors affecting the growth of crops and livestock, as well as their complex relationships. It is a grand challenge to understand and explain smart agriculture. There are numerous models and frameworks proposed for smart agriculture. Some of the problems with the existing studies include: (1) a lack of unified model or framework for smart agriculture, (2) an overlook of the involvement of the most important elements of any agriculture, namely, smart people, and (3) an insufficient consideration of the crucial role of smart government in modern agriculture. In this paper, we propose a Machine-People-Government triangular model for smart agriculture (MPG4SA), emphasizing the roles of machines, human contributors, and government. We introduce a conceptual three-level framework based on the Symbols-Meaning-Value (SMV) space for smart agriculture named SMV4SA. This framework delineates the nine critical roles of machine, people, and government across three layers: data acquisition, knowledge discovery, and decision-making. The framework may provide conceptual and theoretical support for end-to-end smart agriculture applications such as pre-production planning, in-season management, crop disease and pest recognition, post-production management, and government policy making.