Modeling of artificial neural networks and image processing algorithms for bread wheat quality classification

Wheat, which is the most grown agricultural product in the world and in our country, has an important place in terms of human nutrition throughout the world due to its high nutritional value, variety of variations and being at the forefront of basic foodstuffs. Soft wheat, which is used for bread flour production, is the most produced wheat type in our country. Wheat quality is the most important factor for both the farmer, the flour producer and the consumer. Quality criteria are determined by physical and chemical analysis and can only be determined by comprehensive laboratories, technical personnel and experts. This process takes a long time and increases the costs. This study proposes two different models to investigate the feasibility of bread wheat quality classification with artificial intelligence. The model developed with artificial neural networks created a decision support mechanism for the expert who made the final decision, and the model developed with Image Processing shows the feasibility of bread wheat quality classification without the need for laboratory analysis. The results obtained in both studies show that artificial intelligence technologies that can contribute to the digitalization of flour production facilities are feasibility in order to be used as a high-accuracy decision support mechanism. Key Words: Bread Wheat, Quality, Classification, Artificial Neural Network, Image Processing