ABSTRACT: 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 (scari
