A parallelization of orchard temperature predicting programs

Abstract

Frost protection is a significant concern among tree-fruit growers. In order to protect orchards from frost, temperature sensor networks have gained popularity to judge when and where to turn on wind generators and sprinklers. Currently these sensor networks only give growers a real-time alert, however such sensor networks would be more effective if they were integrated in a computation loop that uses past and current temperature data for predicting every overnight transition of orchard air temperature. Although a couple of algorithms using artificial neural network and empirically-formulated polynomials are available to the public, they need to be parallelized for useful on-the-fly prediction. To utilize a cluster of multi-core computing nodes (available through cloud services), we are developing MASS, a library for multi-agent spatial simulation, and parallelizing temperature prediction programs with MASS. This paper demonstrates the MASS library's suitability to parallelization of temperature prediction programs for on-the-fly sensor-data analysis by (1) porting the programs to MASS, (2) running them in a multi-core system, (3) feeding real-time sensor data to them, and (4) measuring their analyzing performance.