Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models.

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical-chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains.

Thick layer drying and storage of rice grain cultivars in silo-dryer-aerator: Quality evaluation at low drying temperature.

Drying rice in a single layer in a silo-dryer-aerator allows uniform drying. The objective of this study was to evaluate the physical, physicochemical, and morphological quality of rice grain cultivars (IRGA 424, BRS Pampeira, and Guri INTA) in the lower (initial time) and upper (final time) layers in a silo-dryer-aerator, employing single-layer loading at low temperatures, using the methods of near-infrared spectroscopy, X-ray diffraction analysis, scanning electron microscopy, and multivariate statistical analysis. Drying rice in silo-dryer-aerator attenuated the moisture diffusivity in the grains, minimizing its effects on the physical, physicochemical, and morphological properties of the grains. However, the physicochemical constituents and morphology of starch were preserved by the low drying temperatures, mainly in the lower layers throughout the 2-month drying. The rice grains of the Guri INTA and BRS Pampeira cultivars were the most resistant to drying and showed greater uniformity on the final quality.