Parallel Computational Structure and Semantics For Soil Quality Analysis Based On LoRa and Apache Spark

A large volume of data is being produced by agrometeorological stations, satellites, Unmanned Aerial Vehicles (UAV), agricultural machines, among other equipment's, all used for agricultural management in food production. Related to that, actually, there are demands for the organization of new computational techniques for agricultural risk management. Therefore, the data volume is immense, and there are challenges to execute them based on both conventional algorithms and architectures. This paper presents the organization of a new parallel model based on semantics and the Apache Spark framework for agricultural risk management related to soil quality. Based in such concept, a parallel algorithm is proposed to distribute tomographic images of soil to several nodes, as well as, to ask them for not only pore recognition, but also for soil density and soil compaction measurements from those images. Besides, the developed parallel algorithm allowed to obtain a better performance gains and reduction of the time required to generate maps for agricultural soil quality, i.e., to support decision making in such field of interest.