Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models.

The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help optimize processes and obtain more accurate results for decision-making regarding the processing and conservation of stored seeds. Therefore, the aim was to assess and predict the physical properties (moisture content, seed mass, length, thickness, width, volume, apparent specific mass, projected area, sphericity, mean diameter, circular area, circularity, drag coefficient), and physicochemical quality (crude protein, ash content, and acidity index) of Jatobá-do-Cerrado seeds under different processing conditions with pulp, without pulp (scarification), without pulp (fermented), and storage conditions at 10 and 23 °C over six months.

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.