Abstract
Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis.