Using the watershed method in determining the size of intersecting particles of crushed grain
https://doi.org/10.30766/2072-9081.2025.26.2.404-414
Abstract
The determination of the granulometric composition of ground grain is of great importance when feeding farm animals and poultry. The sieve method of determining the granulometric composition is traditionally used. With the development of modern digital technologies, it has become possible to determine the granulometric composition of grain without using special sieves. We have proposed a method for determining the particle sizes of crushed grain using color clustering and morphological segmentation. However, this method does not solve the problem of particle overlap, when several contacting components are perceived as one large particle, which ultimately affects the final result of the analysis and gives a serious error relative to the traditional sieving method. In this regard, it was decided to improve the method by additional image analysis using the watershed method. The article presents the results of estimating the particle sizes of crushed grain when they intersect and overlap each other. The sizing technique consisted in the implementation of the following operations: image acquisition; entropy filtering of a grayscale image; calculation of the average color value for each color cluster of the image; construction of a refined binary mask; calculation of the gradient representation of the image with grains; calculation of foreground markers; calculation of background markers; removal from the gradient representation of the image all local brightness minima outside the constructed markers; application of the watershed method; determination of the area and maximum diameter of the Feret and sorting of all areas by size. As a result of the analysis of images of crushed grain particles, histograms of the particle distribution by their areas and lengths were constructed according to the proposed method. The complexity of estimating the size of crushed grain by existing classical methods has been revealed and it has been established that the watershed method gives a sufficiently high error (about 32 %) in determining the particle area, but can be used to determine the linear particle sizes through which their equivalent diameter can be expressed.
Keywords
About the Authors
S. Yu. BulatovRussian Federation
Sergey Yu. Bulatov, PhD in Engineering, Associate Professor, Professor of the Department of Technical Service
Oktyabrskaya str., 22, Knyaginino, Nizhny Novgorod Region, 606340
V. G. Krestinkov
Russian Federation
Vasily G. Krestinkov, Postgraduate Student
Oktyabrskaya str., 22, Knyaginino, Nizhny Novgorod Region, 606340
G. S. Malyshev
Russian Federation
Grigory S. Malyshev, Design Engineer
Burnakovsky Proezd, 15, Nizhny Novgorod, 606340
O. A. Tareeva
Russian Federation
Oksana A. Tareeva, PhD in Engineering, Associate Professor, Associate Professor of the Department of Technical Systems and Technologies
Oktyabrskaya str., 22, Knyaginino, Nizhny Novgorod Region, 606340
A. E. Shamin
Russian Federation
Anatoly E. Shamin, DSc in Economics, Professor, Professor of the Department of Economics and Automation of Business Processes
Oktyabrskaya str., 22, Knyaginino, Nizhny Novgorod Region, 606340
References
1. Abuov S. K., Babadzhanova N. Modern aspects of dairy cow feeding. Eo ipso. 2023;(10):89–90. (In Russ.). URL: https://elibrary.ru/item.asp?id=54623135
2. Burda T. M. Analysis of methods of preparation and distribution of feed mixtures. Scientific research of students in solving urgent problems of the agro-industrial complex: Proceedings of the All-Russian student scientific- practical conference. In IV vol. Molodezhny. Irkutsk: Irkutskiy GAU im. A. A. Ezhevskogo, 2022. pp. 182–189. URL: https://elibrary.ru/item.asp?id=49081866
3. Efimova D. V., Fedorov V. G., Manurikov Ya. N. Feeding livestock with robotic milking. Current issues in the development of agro-industrial, chemical and forestry complexes: collection of abstracts of scientific research-practical conference of young scientists and specialists. Velikiy Novgorod: Novgorodskiy GU imeni Yaroslava Mudrogo, 2021. pp. 115–117. URL: https://elibrary.ru/item.asp?id=48169518&pff=1
4. Tunguchbekova Zh. T., Ibraeva Zh., Murzubraimov B. M., Ysmanov E. M., Shabdanova E. A. Determination of the particular composition of the filter cake by the sieve method. Byulleten' nauki i praktiki = Bulletin of Science and Practice. 2023;9(5):388–394. (In Russ.). DOI: https://doi.org/10.33619/2414-2948/90/48
5. Abalikhin A. M., Barabanov D. V., Krupin A. V., Mukhanov N. V. Evaluation of performance efficiency of centrifugal feed grain grinder. AgroEkoInzheneriya = AgroEcoEngineering. 2024;(1(118)):43–57. (In Russ.). DOI: https://doi.org/10.24412/2713-2641-2024-1118-43-56
6. Sabiev U. K., Sadov V. V. Feeding grain grinder efficiency indices. Vestnik Altayskogo gosudarstvennogo agrarnogo universiteta = Bulletin of Altai State Agricultural University. 2021;(6(200)):93–99. (In Russ.). URL: https://elibrary.ru/bszfpo
7. Savinykh P. A., Turubanov N. V., Moshonkin A. M. Determination of optimal design and technological parameters of a hammer crusher with sieves in end surfaces. Agroinzheneriya = Agricultural Engineering (Moscow). 2023;25(5):17–22. (In Russ.). DOI: https://doi.org/10.26897/2687-1149-2023-5-17-22
8. Ivanov Yu. A., Bulatov S. Yu., Tareeva O. A., Malyshev G. S. Application of the color segmentation method at the feed mixture uniformity determination task. Tekhnika i tekhnologii v zhivotnovodstve = Machinery and technologies in livestock. 2024;(1(53)):54–63. (In Russ.). DOI: https://doi.org/10.22314/27132064-2024-1-54
9. Mohanty S. S., Tripathy S. Application of Different Filtering Techniques in Digital Image Processing. Journal of Physics: Conference Series. 2021;2062:012007. DOI: https://doi.org/10.1088/1742-6596/2062/1/012007
10. Pierre F., Amendola M., Bigeard C., Ruel T., Villard P.-F. Segmentation with Active Contours. Image Processing On Line. 2021;1:120–141. DOI: https://doi.org/10.5201/ipol.2021.298
11. Mohanty S. S., Tripathy S. Application of Watershed Algorithm in Digital Image Processing. Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. 2022;428:401–410. DOI: https://doi.org/10.1007/978-981-19-2225-1_36
12. Wu Y., Li Q. The algorithm of watershed color image segmentation based on morphological gradient. Sensors. 2022;22(21):8202. DOI: https://doi.org/10.3390/s22218202
13. Said K.A.M., Jambek A. B. Analysis of Image Processing Using Morphological Erosion and Dilation. Journal of Physics: Conference Series. 2021;2071:012033. DOI: https://doi.org/10.1088/1742-6596/2071/1/012033
14. Zou L., Song L.-T., Weise T., Wang X.-F., Huang Q.-J., Deng R., Wu Z.-Z. A survey on regional level set image segmentation models based on the energy functional similarity measure. Neurocomputing. 2021;452:606–622. DOI: https://doi.org/10.1016/j.neucom.2020.07.141
Review
For citations:
Bulatov S.Yu., Krestinkov V.G., Malyshev G.S., Tareeva O.A., Shamin A.E. Using the watershed method in determining the size of intersecting particles of crushed grain. Agricultural Science Euro-North-East. 2025;26(2):404–414. (In Russ.) https://doi.org/10.30766/2072-9081.2025.26.2.404-414