The aim of the research presented in this work was to develop a model of Artificial Neural Networks (ANN) with the use of computer image analysis for the qualitative classification of deep-frozen raw material - breaded pollock cutlets (Backfisch). Shape and color discriminants were selected, by using a computer program, it was possible to obtain numerical data and build a learning set from them. This work is an example of using one of the methods of artificial intelligence in the food industry. The designed network was characterized by a very high ability for classification, its training was done by the technique of the so-called " with a teacher". Such actions are motivated by the requirements of consumers who are becoming more and more attentive to the products they consume and expect calorically balanced and very high quality products.
Comparative research was carried out on three methods of acquiring visual data on the shape of obstacles in wastelands and forest areas for automatic control of the stability of self-propelled machines. The tests were carried out for the following sensors: ultrasonic, 3D IFM O3M251 sensor, and Intel RealSense D455 sensor. The sensors were mounted on a common beam and positioned inline above the obstacles. Four cylindrical objects of various structures and sizes, placed on a driven belt conveyor, were used as recognition obstacles. Tests were carried out at two operating speeds. As a result of the research, obstacle heights were identified concerning relation to their movement under the sensors on the conveyor belt. The obtained results allowed for the assessment of the suitability of each of the methods in the context of their use as a substrate monitoring system for the stabilization systems of agricultural and forest self-propelled machines.
Recently the demand for fruit and vegetable juice powders has increased significantly as there are numerous benefits of using these products in various forms of food. Therefore, it is important to optimise spray drying techniques and find how processing factors influence the quality of powders. For this reason, researchers seek modern methods to aid the assessment of quality of food powders. In this study classes of raspberry powders were distinguished on the basis of selected physical parameters such as: colour expressed in the CIE L* a* b* system, moisture content, and water activity. The classification accuracy of the neural models developed in this study was over 96%.
Application of more and more advanced information technologies in agriculture comprises broader and broader range of production processes, planning processes, monitoring and marketing processes. The applied information technologies are used in production technology of animals and crop production. Within the recent decades one can observe a dynamic development of research on artificial intelligence, and at the same time the development of research within the range of advisory systems (expertise), as well the use of methods of computer image analysis in the process of quality assessment/evaluation of vegetables and fruits. One of the areas of using computer image analysis is supporting decision making processes within the range of quality evaluation of agri-food products. The aim of the project was to use image analysis and artificial neural networks to classify quality of convective dried carrot.
Research was conducted for the purpose of qualitative identification of convection-dried strawberries using artificial neural networks. 2 samples of raw material were subjected to a drying process, each representing different qualitative classes: ripe and overripe fruit. The generated MLP neural network was based on shape and color characteristics; 11 parameters of the quality of dried strawberry were specified. Empirical data was obtained from digital images which served as learning sets for the artificial neural networks simulator. The created neural network was to identify individual learning cases as one of the following cases: "good" - ripe or "bad" - overripe strawberry. Furthermore, a correlation analysis was performed, which showed a strong relationship between some variables.
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