The paper presents a method of using vision techniques and artificial neural networks to assess the degree of contamination of cereal grain during grain reception and storage. The aim of the work is to optimize the management of the process of evaluation of contaminants in grain mass in the warehouse and during purchase using vision techniques based on computer image analysis in order to expedite laboratory work. On the basis of the conducted research and analysis of the results, a technology of optimization of quality assessment of stored grain using vision techniques as a quick assessment method was developed. Artificial neural networks were used to analyze the obtained results. Implications for marketing managers and the farming enterprise for sustainable practices and economic advantages are discussed.