Cyber-Physical System Based Data Mining and Processing Toward Autonomous Agricultural Systems

Recent advancements in the internet and the proliferation of sensing devices have made it possible to deploy and communicate heterogeneous and bridge data in a variety of systems that link physical objects to the real world. This breakthrough also provides a lot of benefits for the farming industry, including improved resource leadership and human workforce. With objective evidence gleaned by sensors with the goal of increasing production and durability, principal benefits emerge. This type of automated and data-driven farm management relies on data to boost efficiency while reducing resource waste and environmental contamination. Smart farming, along with automated alternatives that use Artificial Intelligence (AI) approaches, lays the foundation for future food production. This paper proposed an Autonomous Agricultural Cyber-Physical System (AA-CPS) as a framework to predict the precise crop that fits the farm based on the soil and weather data. The data is collected by sensory technology, associated data mining techniques, and autonomous tractors in the field leveraging the best recommendation about what crop to be grown on any farm around the world.