Counting touching wheat grains in images based on elliptical approximation

The number of grains of a cereal plant characterizes its yield, while grain size and shape are closely related to its weight. To estimate the number of grains, their shape and size, digital image analysis is now generally used. The grains in such images may be completely separated, touching or densely packed. In the first case, the simplest binarization/segmentation algorithms, such as the watershed algorithm, can achieve high accuracy in segmentation and counting grains in an image. However, in the case of touching grains, simple machine vision algorithms may lead to inaccuracies in determining the contours of individual grains. Therefore, methods for accurately determining the contours of individual grains when they are in contact are relevant. One approach is based on the search for pixels of the grain contact area, in particular, by identification of concave points on the grain contour boundary. However, some grains may have chips, depressions and bulges, which leads to the identification of the corner points that do not correspond to the grain contact region. Additional data processing is required to avoid these errors. In this paper, we propose an algorithm for the identification of wheat grains in an image and determine their boundaries in the case when they are touching. The algorithm is based on using a modification of the concave point search algorithm and utilizes a method of assigning contour boundary pixels to a single grain based on approximation of grain contours by ellipses. We have shown that the proposed algorithm can identify grains in the image more accurately compared to the algorithm without such approximation and the watershed algorithm. However, the time cost for such an algorithm is significant and grows rapidly with increasing number of grains and contours including multiple grains.

1. Afonnikov D.A., Genaev M.A., Doroshkov A.V., Komyshev E.G., Pshenichnikova T.A. Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments. Russ J Genet. 2016;52(7):688-701. doi 10.1134/S1022795416070024

2. Afonnikov D.A., Komyshev E.G., Efimov V.M., Genaev M.A., Koval V.S., Gierke P.U., Börner A. Relationship between the characteristics of bread wheat grains, storage time and germination. Plants. 2022;11(1):35. doi 10.3390/plants11010035

3. Brinton J., Uauy C. A reductionist approach to dissecting grain weight and yield in wheat. J Integr Plant Biol. 2019;61(3):337-358. doi 10.1111/jipb.12741

4. Cervantes E., Martín J.J., Saadaoui E. Updated methods for seed shape analysis. Scientifica. 2016;2016(1):5691825. doi 10.1155/2016/5691825

5. Comaniciu D., Meer P. Mean shift analysis and applications. In: Proceedings of the Seventh IEEE International Conference on Computer Vision. Vol. 2. Kerkyra, Greece, 1999;1197-1203. doi 10.1109/ICCV.1999.790416

6. Domasev M.V., Gnatyuk S.P. Color, Color Management, Color Calculations and Measurements. St. Petersburg: Piter Publ., 2009 (in Russian)

7. Fisenko V.T., Fisenko T.Yu. Computer Processing and Image Re cognition. St. Petersburg, 2008 (in Russian)

8. Gao L., Zhao C., Liu M. Segmentation of touching seeds based on shape feature and multiple concave point detection. In: 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, 2017;1-5. doi 10.1109/IST.2017.8261448

9. Gedraite E.S., Hadad M. Investigation on the effect of a Gaussian Blur in image filtering and segmentation. In: Proceedings ELMAR-2011, Zadar, Croatia, 2011;393-396

10. Gonzalez R.C., Woods R.E. Digital Image Processing. CRC Press, Boca Raton, FL, 2004

11. Herridge R.P., Day R.C., Baldwin S., Macknight R.C. Rapid analysis of seed size in Arabidopsis for mutant and QTL discovery. Plant Methods. 2011;7(1):3. doi 10.1186/1746-4811-7-3

12. Howse J. OpenCV Computer Vision with Python. Birmingham: Packt Publishing, 2013

13. Kolhar S., Jagtap J. Plant trait estimation and classification studies in plant phenotyping using machine vision. A review. Inf Process Agric. 2023;10(1):114-135. doi 10.1016/j.inpa.2021.02.006

14. Komyshev E.G., Genaev M.A., Afonnikov D.A. Evaluation of the SeedCounter, a mobile application for grain phenotyping. Front Plant Sci. 2017;7:1990. doi 10.3389/fpls.2016.01990

15. Komyshev E.G., Genaev M.A., Afonnikov D.A. Analysis of color and texture characteristics of cereals on digital images. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov J Genet Breed. 2020;24(4):340- 347. DOI 10.18699/VJ20.626

16. Li Z., Guo R., Li M., Chen Y., Li G. A review of computer vision technologies for plant phenotyping. Comput Electron Agric. 2020;176: 105672. doi 10.1016/j.compag.2020.105672

17. Liang N., Sun S., Yu J., Taha M.F., He Y., Qiu Z. Novel segmentation method and measurement system for various grains with complex touching. Comput Electron Agric. 2022;202:107351. doi 10.1016/j.compag.2022.107351

18. Lin W., Ma D., Su Q., Liu S., Liao H., Yao H., Xu P. Image segmentation method for physically touching soybean seeds. Software Impacts. 2023;18:100591. doi 10.1016/j.simpa.2023.100591

19. Liu T., Chen W., Wang Y., Wu W., Sun C., Ding J., Guo W. Rice and wheat grain counting method and software development based on Android system. Comput Electron Agric. 2017;141:302-309. doi 10.1016/j.compag.2017.08.011

20. Mebatsion H.K., Paliwal J., Jayas D.S. Automatic classification of nontouching cereal grains in digital images using limited morphological and color features. Comput Electron Agric. 2013;90:99-105. doi 10.1016/j.compag.2012.09.007

21. Otsu N. A threshold selection method from gray-level histograms. In: IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1): 62-66. doi 10.1109/TSMC.1979.4310076

22. Qin Y., Wang W., Liu W., Yuan N. Extended-maxima transform watershed segmentation algorithm for touching corn kernels. Adv Mech Eng. 2013;5:268046. doi 10.1155/2013/268046

23. Roerdink J.B.T.M., Meijster A. The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inform. 2000; 41(2):187-228

24. Tan S., Ma X., Mai Z., Qi L., Wang Y. Segmentation and counting algorithm for touching hybrid rice grains. Comput Electron Agric. 2019;162:493-504. doi 10.1016/j.compag.2019.04.030

25. Tanabata T., Shibaya T., Hori K., Ebana K., Yano M. SmartGrain: highthroughput phenotyping software for measuring seed shape through image analysis. Plant Physiol. 2012;160(4):1871-1880. doi 10.1104/pp.112.205120

26. Wang W., Paliwal J. Separation and identification of touching kernels and dockage components in digital images. Can Biosyst Eng. 2006;48:7

27. Whan A.P., Smith A.B., Cavanagh C.R., Ral J.P.F., Shaw L.M., Howitt C.A., Bischof L. GrainScan: a low cost, fast method for grain size and colour measurements. Plant Methods. 2014;10:23. doi 10.1186/1746-4811-10-23

28. Yang S., Zheng L., He P., Wu T., Sun S., Wang M. High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning. Plant Methods. 2021;17(1):50. doi 10.1186/s13007-021-00749-y

29. Zhang J., Liu S., Wu W., Zhong X., Liu T. Research on a rapid identification method for counting universal grain crops. PLoS One. 2022;17(9):e0273785. doi 10.1371/journal.pone.0273785

30. Zhang X., Deng Z., Wang Y., Li J., Tian J. Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds. Genetica. 2014; 142(4):371-379. doi 10.1007/s10709-014-9781-6