АНАЛИЗ ФУНКЦИОНИРОВАНИЯ СИСТЕМ МАШИННОГО ЗРЕНИЯ ДЛЯ ОЦЕНКИ КАЧЕСТВА СЕЛЬСКОХОЗЯЙСТВЕННЫХ ПРОДУКТОВ

Authors

  • Мухамедханов Улугбек Тургудович ТГТУ имени Ислама Каримова
  • Сувонов Бехруз Искандар угли Университет экономики и педагогики
  • Дошанова Малика Юлдашовна ТУИТ имени Мухаммада ал-Хоразмий

Keywords:

Машинное зрение, классификация, сортировка, зерно, анализ и обработка изображений, реальное время

Abstract

Рассматриваются преимущества и достижения в области технологий машинного зрения для оценки и классификации качества продуктов питания в сельском хозяйстве. Обсуждаются основные требования к автоматизированным системам машинного зрения. Приводятся примеры применения для классификации и сортировки зерновых и других пищевых продуктов.

References

1. Baranyai L. Visual inspection of grains by digital image processing. Ph.D. Thesis, SzIE University, Budapest.

2. Blasco J., S. Cubero, J. Gomez-Sanchis, P. Mira, E. Molto, Development of a machine for automatic sorting of pomegranate (Punica granatum) arils based on computer vision, Journal of Food Engineering 90 (2009) 27–34

3. Brosnan T., da-Wen Sun, Inspection and grading of agricultural and food products by computer vision systems – a review, Computers and Electronics in Agriculture 36 (2002), 193–213

4. Chen, P., McCarthy, M.J., Kauten, R., NMR for internal quality evaluation of fruits and vegetables. Trans. ASAE 32, 1989, 1747–1753.

5. Cheng–Jin Du, Da Wen Sun, Recent developments in the applications of image processing techniques for food quality evaluation, Trends in Food Science and Technology 15 (2004), 230–249.

6. Chong V. K., N. Kondo, K. Ninomiya, M. Monta, K. Namba, Comparison on eggplant fruit grading between NIR-color camera and color camera, Proceeding of the 2004 conference, ASAE.

7. Diaz R., G. Faus, M. Blasco, J. Blasco, E. Molto, The application of a fast algorithm for the classification of olives by machine vision, Food Research International 33 (2000), 305–309

8. Falasconi M., G. Sberreglieri., Toxins detection in cereals by electronic nose: in vitro study., University of Brescia & INFM-CNR, Italy, 2004, Web: http://tlab.ing.unibs.it.

9. Liming Xu, Z. Yanchao, Automated strawberry grading system based on image processing, Computers and Electronics in Agriculture 71S(2010), S32–S39

10. Majumdar S., D.S. Jayas Classification of bulk samples of cereal grains using machine vision, J. Agric. Engng. Res. (1999) 73, 35–47

11. Manickavasagam A., G. Sathya, D.S. Jayas, N.D.G. White Wheat class identification using monochrome images, Journal of Cereal Science 47 (2008), 518–527.

12. Nagata, M., Cao, Q., Bato, P.M., Shrestha, B.P., Kinoshita, O. Basic study on strawberry sorting system in Japan. In: Annual International Meeting Technical Papers, Paper No. 973095, ASAE, (1997)

13. Nelson B., N. Hess. Automated official grain inspection system, Annual International meeting, ASAE, 2005.

14. Ni, B., Paulsen, M.R., Reid, J.F., Size grading of corn kernels with machine vision. In: Annual International Meeting Technical Papers, Paper No. 973046, 1997 ASAE

15. Pearson, T., Toyofuku, N., Automated sorting of pistachio nuts with closed shells. Applied Engineering in Agriculture 16 (1), 2000, 91–94.

16. Pearson T., Hardware based image processing for high-speed inspection of grains, Computers and electronics in agriculture 62 (2009), p. 12–18.

17. Pittet A., Modern methods and trends in mycotoxin analysis, 117th annual conference of the society of food environmental chemistry, 8-September, 2005, p. 424–444.

18. Rigney M. P., G. H. Brusewitz, G. A. Kranzler, Asparagus defect inspection with machine vision. Transactions of the ASAE, 35, 1873–1878, 1992.

19. Riquelme M.T., P. Barreiro, M. Ruiz-Altisent, C. Valero Olive classification according to external damage using image analysis, Journal of Food Engineering 87 (2008) 371–379

20. Schatzki, T.F., Haff, R.P., Young, R., Can, I., Le, L.C., Toyofuku, N., Defect detection in apples by means of X-ray imaging. Trans. ASAE 40, 1997. 1407–1415.

21. Sun, D.-W., Inspecting pizza topping percentage and distribution by a computer vision method, Journal of Food Engineering 44 (2000) p.245–249.

22. Taghizadeh Masoud, A. Gowen, C. O'Donnell, Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms, Biosystems engineering 108 (2011), 191–194.

23. Tao, Y., Morrow, C.T., Heinemann, P.H., Sommer, H.J., Fourier based separation techniques for shape grading of potatoes using machine vision. Transactions of the ASAE 38 (3), 1995, 949–957.

24. Wan Y.-N., Kernel handling performance of an automatic grain quality inspection system, Transactions of the ASAE Vol. 45(2), p. 369–377.

25. Wan Y.-N, C.-M Lin, J.-F. Chiou , 2002, Rice quality classification using an automatic grain quality inspection system , Transactions of the ASAE Vol. 45(2): 379–387.

26. Yang Chang, S. Delwiche, S. Chen, I. Martin Lo, Enhancement of Fusarium head blight detection in free-falling wheat kernels using a bichromatic pulsed LED design, Optical Engineering Vol.48(2), 023602, 2009.

27. Zhang, N., Chaisattapagon, C., Effective criteria for weed identification in wheat fields using machine vision. Transactions of the ASAE 38 (3), 1995, 965–974.

28. Zou Xiao-bo, Z. Jie-wen, L. Yanxiao, M. Holmes, In-line detection of apple defects using the color cameras system, Computers and Electronics in Agriculture 70 (2010) 129–134.

Downloads

Published

2025-08-28

How to Cite

АНАЛИЗ ФУНКЦИОНИРОВАНИЯ СИСТЕМ МАШИННОГО ЗРЕНИЯ ДЛЯ ОЦЕНКИ КАЧЕСТВА СЕЛЬСКОХОЗЯЙСТВЕННЫХ ПРОДУКТОВ. (2025). DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 3(4), 224-231. https://dtai.tsue.uz/index.php/dtai/article/view/770