Prototype wireless sensor network and Internet of Things platform for real-time monitoring of intergranular equilibrium moisture content and predict the quality corn stored in silos bags

Abstract

Grain storage in bag silos has increased in recent years, mainly due to the low initial investment cost. However, there is no control of the ecosystem that involves the biotic and abiotic factors of storage. Thus, the objective was to develop and validate a prototype wireless sensor network and Internet of Things (IoT) platform for real-time monitoring of intergranular equilibrium moisture content and predict through neural network algorithms the physical, physical quality-chemical and microbiological mass of corn stored in bag silos. For an evaluation over three months, the experiments were installed with corn grains with two initial moisture contents of 13 % and 18 % (w.b.), three storage environments with temperatures of 17, 23, and 30 °C in bag silos. It was observed during the monitoring of stored grains, variations of moisture balance hygroscopic that indirectly inferred the quality of corn. The prototype and device with temperature sensors and intergranular relative humidity of the grains stored in bag silos were adjusted, obtaining satisfactory results for the determination of the equilibrium moisture content curves of the mass of corn grains stored, in real-time, connected to an IoT platform, for indirect monitoring of the quality of stored corn grains over time. In the moisture contents of 13 % and the storage condition of 17 °C they had the best quality results, while in the storage in bag silos with moisture contents of 13 % and 18 % showed no differences in the condition of 23 °C. However, at a temperature of 30 °C, the grains suffered a high deterioration. Furthermore, the quality prediction results using Artificial Neural Networks algorithms, indicated a high coefficient of determination of the trained models, presenting itself as a promising perspective, mainly in develop embedded technologies for monitoring and predicting qualitative variables of corn stored in bag silos.