Integration of traditional and computer methods in optimization of wheat grain moistening processes

Mathematical modeling and software implementation to improve the efficiency of grain moistening

Authors

  • Abdushukur Urinboev TATU Farg'ona filiali
  • Б.Р. Исмаилов

Keywords:

hydrothermal treatment, grain moisture,, computer modeling, grain characteristics, technological process

Abstract

This study investigates the hydrothermal treatment of wheat grain to optimize moistening conditions and enhance flour quality. Utilizing both conventional analytical methods and advanced computational modeling techniques, specifically COMSOL Multiphysics, the research examines moisture distribution dynamics across different grain layers (shell, endosperm, and germ) and their impact on milling efficiency. Experimental findings indicate that cold and high-speed conditioning offer distinct advantages depending on the physicochemical properties of the grain. 

 

References

. Chateigner-Boutin, A.-L., Lapierre, C., Alvarado, C., Yoshinaga, A., Barron, C., Bouchet, B., Bakan, B., Saulnier, L., Devaux, M.-F., Girousse, C., & Guillon, F. (2018). Ferulate and lignin cross-links increase in cell walls of wheat grain outer layers during late development. Plant Science, 276, 199-207. https://doi.org/10.1016/j.plantsci.2018.08.022

. James, C., Smith, D., He, W., Chandra, S. S., & Chapman, S. C. (2024). GrainPointNet: A deep-learning framework for non-invasive sorghum panicle grain count phenotyping. Computers and Electronics in Agriculture, 217, 108485. https://doi.org/10.1016/j.compag.2023.108485

. Shi, J., Ding, Z., Ge, X., Qiu, X., Xu, J., Xiao, L., Liu, L., Tang, L., Cao, W., Zhu, Y., & Liu, B. (2024). Compound extreme heat and drought stress alter the spatial gradients of protein and starch in wheat grains. Agricultural Water Management, 303, 109049. https://doi.org/10.1016/j.agwat.2024.109049

. Kang, S., Kim, Y., Ajani, O. S., Mallipeddi, R., & Ha, Y. (2024). Predicting the properties of wheat flour from grains during debranning: A machine learning approach. Heliyon, 10(17), e36472. https://doi.org/10.1016/j.heliyon.2024.e36472

. Shi, T., Gao, Y., Song, J., Ao, M., Hu, X., Yang, W., Chen, W., Liu, Y., & Feng, H. (2024). Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains. Food Chemistry, 461, 140651. https://doi.org/10.1016/j.foodchem.2024.140651

. Wang, Y., Ou, X., He, H.-J., & Kamruzzaman, M. (2024). Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chemistry: X, 21, 101235. https://doi.org/10.1016/j.fochx.2024.101235

. Zhang, C., Yi, Y., Wang, L., Chen, S., Li, P., Zhang, S., & Xue, Y. (2024). Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery. Smart Agricultural Technology, 9, 100581. https://doi.org/10.1016/j.atech.2024.100581

. Wang, W., Huang, Z., Fu, Z., Jia, L., Li, Q., & Song, J. (2024). Impact of digital technology adoption on technological innovation in grain production. Journal of Innovation & Knowledge, 9(3), 100520. https://doi.org/10.1016/j.jik.2024.100520

. Shah, S. A. A., Luo, H., Pickupana, P. D., Ekeze, A., Sohel, F., Laga, H., Li, C., Paynter, B., & Wang, P. (2022). Automatic and fast classification of barley grains from images: A deep learning approach. Smart Agricultural Technology, 2, 100036. https://doi.org/10.1016/j.atech.2022.100036

. Kumar, G., Le, D. T., Durco, J., Cianciosi, S., Devkota, L., & Dhital, S. (2023). Innovations in legume processing: Ultrasound-based strategies for enhanced legume hydration and processing. Trends in Food Science & Technology, 139, 104122. https://doi.org/10.1016/j.tifs.2023.104122

. Guo, Y., Xiao, Y., Hao, F., Zhang, X., Chen, J., de Beurs, K., He, Y., & Fu, Y. H. (2023). Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 124, 103528. https://doi.org/10.1016/j.jag.2023.103528

. Wu, W., Yang, T.-l., Li, R., Chen, C., Liu, T., Zhou, K., Sun, C.-m., Li, C.-y., Zhu, X.-k., & Guo, W.-s. (2020). Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales. Journal of Integrative Agriculture, 19(8), 1998-2008. https://doi.org/10.1016/S2095-3119(19)62803-0

. Shafaei, S. M., Nourmohamadi-Moghadami, A., Rahmanian-Koushkaki, H., & Kamgar, S. (2019). Neural computing efforts for integrated simulation of ultrasound-assisted hydration kinetics of wheat. Information Processing in Agriculture, 6(3), 357-374. https://doi.org/10.1016/j.inpa.2019.01.001

. Naik, N. K., Sethy, P. K., Behera, S. K., & Amat, R. (2024). A methodical analysis of deep learning techniques for detecting Indian lentils. Journal of Agriculture and Food Research, 15, 100943. https://doi.org/10.1016/j.jafr.2023.100943

. Asefa, B. G., Tsige, F., Mehdi, M., Kore, T., & Lakew, A. (2023). Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique. Smart Agricultural Technology, 3, 100097. https://doi.org/10.1016/j.atech.2022.100097

. Agarwal, D., Sweta, & Bachan, P. (2023). Machine learning approach for the classification of wheat grains. Smart Agricultural Technology, 3, 100136. https://doi.org/10.1016/j.atech.2022.100136

. Laabassi, K., Belarbi, M. A., Mahmoudi, S., Mahmoudi, S. A., & Ferhat, K. (2021). Wheat varieties identification based on a deep learning approach. Journal of the Saudi Society of Agricultural Sciences, 20(5), 281-289. https://doi.org/10.1016/j.jssas.2021.02.008

. Steinberg, T. S., Morozova, O. V., & Semikina, L. I. (2014). Storage and processing of agricultural raw materials. VNIIZ Grain and Products of Its Processing, 310, 47-51. https://vniiz.org/science/publication/article-60

. Wu W, Zhao Y, Wang H, Yang T, Hu Y, Zhong X, Liu T, Sun C, Sun T, Liu S. WG-3D: A Low-Cost Platform for High-Throughput Acquisition of 3D Information on Wheat Grain. Agriculture. 2022; 12(11):1861. https://doi.org/10.3390/agriculture12111861

. Webpupil. (01.2014). Web tutorial. Retrieved October 1, 2024, from https://www.webpupil.ru/article.php?id=14

. Urinboev, A. A., & Ismailov, B. R. (2022). On the problems of developing automated control systems in grain preparation at flour mills. In Proceedings of the International Scientific and Practical Conference "Auezov Readings - 22: Academician Kanysh Satpaev - Founder of Kazakh Science" (pp. 5-7). Shymkent, Republic of Kazakhstan.

Published

2025-03-23

How to Cite

Urinboev, A., & Исмаилов, Б. (2025). Integration of traditional and computer methods in optimization of wheat grain moistening processes: Mathematical modeling and software implementation to improve the efficiency of grain moistening. The Descendants of Al-Fargani, 1(1), 149–160. Retrieved from https://al-fargoniy.uz/index.php/journal/article/view/805