Electrotechnical Tools and Computer Image Analysis in Assessing the Quality of Maize Grain During Storage

Szwedziak, Katarzyna; Dolezal, Petr; Tabor, Sylwester; Ogrodniczek, Jacek

doi:10.2478/agriceng-2023-0016

Abstract

The study of qualitative characteristics is becoming increasingly important due to determination of the purchase price and further use of seeds. An important problem of the modern sustainable agriculture is the production of seeds and products with appropriate quality parameters. The research carried out so far proves that the technology of harvesting, transport, and drying conditions as well as storage have an impact on the quality of seeds, determining their usefulness for the industry. The smallest irregularities can cause irreversible changes and significantly reduce the technological value of seeds and their processing products. The use of tools in the field of supporting electrical engineering enables detection and highlighting of image elements so that it becomes readable to the human eye. The aim of the research was to develop technology for evaluating grain in storage using electrotechnical tools and computer techniques.

References

Abdullah, M. Z., Guan, L. C., Lim, K. C., & Karim, A. A. (2004). The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. Journal of food engineering, 61(1), 125-135.
Search in Google Scholar Back to article
Alexandratos, N. & Bruinsma, J. (2012). World Agriculture towards 2030/2050: the 2012 Revision. ESA Working Paper Rome. FAO.
Search in Google Scholar Back to article
Białobrzewski, I. (2005). Wykorzystanie sieci neuronowej do estymacji wartości wilgotności względnej powietrza na podstawie wartości jego temperatury. Inżynieria Rolnicza, 1(61), 15-22.
Search in Google Scholar Back to article
Białobrzewski, I., Markowski, M. & Bowszys, J. (2005). Symulacyjny model zmian pola temperatury w silosie zbożowym. Inżynieria Rolnicza, 8(60), 23-30.
Search in Google Scholar Back to article
Broda, M., Grajek, W. (2009). Mikroflora ziaren zbóż i metody redukcji skażenia mikrobiologicznego. Zeszyty Problemowe Postępów Nauk Rolniczych, 2, 19–30.
Search in Google Scholar Back to article
Chai, T. (2016). Industrial process control systems: research ststus and development direction. Scientia Sincia Informations, 46(8), 1003-1015.
Search in Google Scholar Back to article
Du, C. J. & Sun, D.-W. (2005). Correlating shrinkage with yield, water content and texture of pork ham by computer vision. Journal of Food Process Engineering, 28, 219-232.
Search in Google Scholar Back to article
Dworczak, M. & Szlasa, R. (2001). Wpływ innowacji na wzrost konkurencyjności przedsiębiorstw. Zarzadzanie innowacjami. Warszawa, PL: Oficyna Wydawnicza Politechniki Warszawskiej.
Search in Google Scholar Back to article
Godfray, H. C. J., & Garnett, T. (2014). Food security and sustainable intensification. Philosophical transactions of the Royal Society B: biological sciences, 369(1639), 20120273.
Search in Google Scholar Back to article
Gonzales-Barron, U., & Butler, F. (2006). Statistical and spectral texture analysis methods for discrimination of bread crumb images. In 13th World Congress of Food Science & Technology 2006 (pp. 164-164).
Search in Google Scholar Back to article
Iqbal, A., Valous, N. A., Mendoza, F., Sun, D.-W. & Allen, P. (2010). Classification of pre-sliced pork and Turkey ham qualities based on image color and textural features and their relationships with consumer responses. Meat Science, 84, 455-465.
Search in Google Scholar Back to article
Kręcidło, Ł., & Krzyśko-Łupicka, T. (2015). Sensitivity of molds isolated from warehouses of food production facility on selected essential oils. Ecological Engineering & Environmental Technology, 43, 100-108.
Search in Google Scholar Back to article
Li, J., Liao, G., Ou, Z., & Jin, J. (2007, December). Rapeseed seeds classification by machine vision. In Workshop on Intelligent Information Technology Application (IITA 2007), pp. 222-226.
Search in Google Scholar Back to article
Liu, Z. Y., Cheng, F., Ying, Y. B., & Rao, X. Q. (2005). Identification of rice seed varieties using neural network. Journal of Zhejiang University-Science B, 6(11), 1095-1100.
Search in Google Scholar Back to article
Majumdar, S., Jayas, D, S. & Symons, S. J. (1999). Textural features for grain identification. Agricultural Engineering Journal, 8(4), 213-222.
Search in Google Scholar Back to article
Manickavasagab, A., Sathya, G., Jayas, D.S. & White, N.D.G. (2008). Wheat class identification using monochrome images. Journal of Cereak Science, 47, 518-527.
Search in Google Scholar Back to article
Mladenov, M., & Dejanov, M. (2004, June). Analysis of the possibilities for separator color and texture features. In Proceedings of the International Conference on Computer Systems and Technologies, Rousse, Bulgaria, pp. 17-18.
Search in Google Scholar Back to article
Mohan, A. L., Jayas, D. S., White, N. D. G., & Karunakaran, C. (2004). Classification of bulk oilseeds, specialty seeds and pulses using their reflectance characteristics. In Proceedings of the International Quality Grain Conference, Indiana, USA, pp. 19-22.
Search in Google Scholar Back to article
Qian, F., Zhong, W. & Du, W. (2017). Fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Elsevier Engineering, 3(2), 154-160.
Search in Google Scholar Back to article
Sànchez, A. J., Albarracin, W., Grau, R., Ricolfe, C. & Barat J. M. (2008). Control of ham salting by using image segmentation. Food Control, 19, 135-142.
Search in Google Scholar Back to article
Szwedziak, K. (2019b). The use of vision techniques for the evaluation of selected quality parameters of maize grain during storage. E3S Web of Conference, 132, 01028.
Search in Google Scholar Back to article
Szwedziak, K. (2019a). Artifical neutral networks and computer image analysis of selected quality parameters of pea seeds. E3S Web of Conference, 132, 01027.
Search in Google Scholar Back to article
Tukiendorf, M. (2005). Zastosowanie sieci FBM w neuronowym modelowaniu mieszania dwuskładnikowych układów ziarnistych. Inżynieria Rolnicza, 9(14), 367-373.
Search in Google Scholar Back to article
Tukiendorf, M., Szwedziak, K., & Sobkowicz, J. (2006). Określenie czystości ziarna konsumpcyjnego za pomocą komputerowej analizy obrazu. Inżynieria Rolnicza, 10, 519-525.
Search in Google Scholar Back to article
Visen, N.S. Paliwal, J., Jayas, D.S. & White, N.D.G. (2004). Image analysis of bulk grain samples using neural networks. Canadian Biosystems Engineering, 46, 11-15.
Search in Google Scholar Back to article

Electrotechnical Tools and Computer Image Analysis in Assessing the Quality of Maize Grain During Storage

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