Abstract
Objectives
The paper presents preliminary results on the assessment of algorithms used in image processing of the grain damage degree. The purpose of the work is developing a tool allowing to analyse sample cross-sections of rye germs.
Methods
The analysis of the grain cross-sections was carried out on the basis of a series their photos taken at equal time intervals at a set depth. The cross-sections will be used to create additional virtual cross-sections allowing to analyse the whole sample volume. The ultimate plan is to generate two cross-sections perpendicular to each other. Based on volumetric data read from the sample section, a three-dimensional model of an object will be generated.
Results
The analysis of model surface will allowed us to detect possible grain damage. The developed method of preparing the research material and the proprietary application allowed for the identification of internal defects in the biological material (cereal grains).
Conclusions
The presented methodology may be used in the agri-food industry in the future. However, much research remains to be done. These works should primarily aim at significantly reducing the time-consuming nature of individual stages, as well as improving the quality of the reconstructed image.
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Research funding: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Not applicable.
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Ethical approval: Not applicable.
References
1. Shashko, YK, Dolgova, AL, Shashko, MN. Direct and indirect losses determining the harmfulness of mushrooms p. Fusarium – fusariosis causes wheat speak and grain. Proc Natl Acad Sci Belarus Agrar Ser 2020;58:55–67. https://doi.org/10.29235/1817-7204-2020-58-1-55-67.Search in Google Scholar
2. Kuznetsova, E, Klimova, E, Bychkova, T, Zomitev, V, Motyleva, S, Brindza, J. Alteration of biochemical parameters and microstructure of Fagopyrum esculentum Moench grain in process of germination. Potravinarstvo 2018;12:687–93. https://doi.org/10.5219/932.Search in Google Scholar
3. Samarah, NH, Alqudah, AM, Al-Mahasneh, MM, Al-Antary, TM. Effect of developmental stage of wheat on seed germination and damage by rhyzopertha dominica F. during storage. Fresenius Environ Bull 2020;29:5038–44.Search in Google Scholar
4. Adesina, JM, Aderibigbe, ATB. Seed preservatives properties of Secamone afzelii (Schult) K. Schum extracts on wheat grains damage and germination capability. Bull Natl Res Cent 2021;45:52. https://doi.org/10.1186/s42269-021-00507-z.Search in Google Scholar
5. Chen, Z, Wassgren, C, Ambrose, K. A review of grain kernel damage: mechanisms, modeling, and testing procedures. Trans ASABE 2020;63:455–75. https://doi.org/10.13031/trans.13643.Search in Google Scholar
6. Paulsen, MR, Nave, WR, Mounts, TL, Gray, LE. Storability of harvest-damaged soybeans. Trans ASAE 1981;24:1583–9. https://doi.org/10.13031/2013.34494.Search in Google Scholar
7. Zhang, X, Zhao, X, Zhang, J, Jiao, W, Shao, Z, Gao, L. Detection of internal mechanical cracks in corn seeds based on data fusion technology. Trans Chin Soc Agric Eng 2012;28:136–41.Search in Google Scholar
8. Li, X, Li, Y, Ma, F, Gao, L. Anti-pressing properties and crack formation law of corn seed. Trans Chin Soc Agric Mach 2011;42:94–8.Search in Google Scholar
9. Li, X, Jie, X, Zhang, Y, Li, F, Gao, L. Detecting and research on characteristics and mechanism of inner mechanical cracks of corn seed kernels. Trans Chin Soc Agric Mach 2010;41:143–7. https://doi.org/10.3969/j.issn.1000-1298.2010.12.030.Search in Google Scholar
10. Smith, JA. Combine adjustments and operation to minimize damage to dry edible bean seed. St. Joseph, Michigan: American Society of Agricultural Engineers; 1997.Search in Google Scholar
11. Kowalczuk, J. Pattern of seed losses and damage during soybean harvest with grain combine harvesters. Int Agrophys 1999;13:103–7.Search in Google Scholar
12. Hermann, D. Optimisation of combine harvesters using model-based control. Lyngby, Denmark: Technical University of Denmark, DTU Elektro; 2018.Search in Google Scholar
13. Wang, K, Xie, R, Ming, B, Hou, P, Xue, J, Li, S. Review of combine harvester losses for maize and influencing factors. Int J Agric Biol Eng 2021;14:1–10. https://doi.org/10.25165/j.ijabe.20211401.6034.Search in Google Scholar
14. Craessaerts, G, Saeys, W, Missotten, B, De Baerdemaeker, J. Identification of the cleaning process on combine harvesters, Part II: a fuzzy model for prediction of the sieve losses. Biosyst Eng 2010;106:97–102. https://doi.org/10.1016/j.biosystemseng.2009.11.009.Search in Google Scholar
15. Zhao, Z, Li, Y, Chen, J, Xu, J. Grain separation loss monitoring system in combine harvester. Comput Electron Agric 2011;76:183–8. https://doi.org/10.1016/j.compag.2011.01.016.Search in Google Scholar
16. Bieniek, J, Banasiak, J, Lewandowski, B, Detyna, J. Cleanness of the grain obtained on the sectional sieve. Acta Agrophysica 2001;46:7–14.Search in Google Scholar
17. Myhan, R, Jachimczyk, E. Grain separation in a straw walker unit of a combine harvester: process model. Biosyst Eng 2016;145:93–107. https://doi.org/10.1016/j.biosystemseng.2016.03.003.Search in Google Scholar
18. Xu, J, Li, Y. A Pvdf sensor for monitoring grain loss in combine harvester. In: IFIP advances in information and communication technology. Computer and Computing Technologies in Agriculture III. Berlin, Heidelberg: Springer; 2010, vol 317:499–505 pp.10.1007/978-3-642-12220-0_72Search in Google Scholar
19. Craessaerts, G, Saeys, W, Missotten, B, De Baerdemaeker, J. Identification of the cleaning process on combine harvesters. Part I: a fuzzy model for prediction of the material other than grain (MOG) content in the grain bin. Biosyst Eng 2008;101:42–9. https://doi.org/10.1016/j.biosystemseng.2008.05.016.Search in Google Scholar
20. Wacker, P. Influence of crop properties on the threshability of cereal crops. In: International conference on crop harvesting and processing. St. Joseph, MI: American Society of Agricultural and Biological Engineers; 2013.10.13031/2013.15181Search in Google Scholar
21. Qamar-uz-Zaman ADC& MAR. Wheat harvesting losses in combining as affected by machine and crop parameters. Pak J Agri Sei 1992;29:1–4.Search in Google Scholar
22. White, GM, Bridges, TC, Loewer, OJ, Ross, IJ. Seed coat damage in thin-layer drying of soybeans. In: Transactions of the ASAE (American Society of Agricultural Engineers). St. Joseph, Michigan: American Society of Agricultural and Biological Engineers; 1980, vol 23:0224–7 pp.10.13031/2013.34559Search in Google Scholar
23. Albaneze, R, Villela, FA, Possenti, JC, Guollo, K, Riedo, IC. Mechanical damage caused by the use of grain carts for transport during soybean seed harvest. J Seed Sci 2018;40:422–7. https://doi.org/10.1590/2317-1545v40n4174101.Search in Google Scholar
24. Otten, L, Brown, R, Reid, WS. Drying of white beans - effect of temperature and relative humidity on seed coat damage. Can Agric Eng 1984;26:101–4.Search in Google Scholar
25. Kirkkari, A-M, Peltonen-Sainio, P, Rita, H. Reducing grain damage in naked oat through gentle harvesting. Agric Food Sci 2001;10:223–9. https://doi.org/10.23986/afsci.5696.Search in Google Scholar
26. Jin, C, Kang, Y, Guo, H, Yin, X. An experimental and finite element analysis of the characteristics of soybean grain compression damage. J Food Process Eng 2021;44:e13721. https://doi.org/10.1111/jfpe.13721.Search in Google Scholar
27. Krot, P, Zimroz, R, Michalak, A, Wodecki, J, Ogonowski, S, Drozda, M, et al.. Development and verification of the diagnostic model of the sieving screen. Shock Vib 2020;2020:1–14. https://doi.org/10.1155/2020/8015465.Search in Google Scholar
28. Bakhtavar, MA, Afzal, I, Basra, SMA, Wahid, A. Implementing the ‘dry chain’ during storage reduces losses and maintains quality of maize grain. Food Secur 2019;11:345–57. https://doi.org/10.1007/s12571-019-00905-2.Search in Google Scholar
29. Paulsen, MR, Nave, WR. Soybean seedcoat damage detection methods. In: Paper - American Society of Agricultural Engineers. St. Joseph, Michigan: American Society of Agricultural Engineers; 1978, 77-3503.Search in Google Scholar
30. Paulsen, MR, Nave, WR, Gray, LE. Soybean seed quality as affected by impact damage. Trans ASAE 1981;24:1577–82. https://doi.org/10.13031/2013.34493.Search in Google Scholar
31. Gao, L, Li, X, Jie, X, Na, X, Zhang, W, Du, X. Inner mechanical damage impact to germination of soybean kernels. Trans Chin Soc Agric Mach 2010;41.Search in Google Scholar
32. De Oliveira, JB, Fernandes, MVA, Teixeira, LRL. Modeling and simulation of a temperature robust control in grain drying systems for thermal damage reduction. In: Proceedings of the 9th international conference on informatics in control, automation and robotics. Rome, Italy: SciTePress - Science and and Technology Publications; 2012:561–5 pp.Search in Google Scholar
33. Woźniak, W, Styk, W. Internal damage to wheat grain as a result of wetting and drying. Dry Technol 1996;14:349–65. https://doi.org/10.1080/07373939608917101.Search in Google Scholar
34. Drapikowski, P. Measurement of medical parameters based on 3D surface models. In: 14th international conference in Central Europe on computer graphics, visualization and computer vision 2006, WSCG’2006 - in co-operation with EUROGRAPHICS, full papers proceedings. Plzen, Czech Republic: University of West Bohemia; 2006:7–9 pp.Search in Google Scholar
35. Drapikowski, P, Czwojdzinski, A. Geometrical and morphological validation of medical parameters measurements based on 3D surface models. Mach Graph Vis 2007;16:207–19.Search in Google Scholar
36. Tuncer, T, Dogan, S, Abdar, M, Ehsan Basiri, M, Pławiak, P. Face recognition with triangular fuzzy set-based local cross patterns in wavelet domain. Symmetry 2019;11:787. https://doi.org/10.3390/sym11060787.Search in Google Scholar
37. Jabłoński, M, Tylek, P, Walczyk, J, Tadeusiewicz, R, Piłat, A. Colour-based binary discrimination of scarified Quercus robur acorns under varying illumination. Sensors 2016;16:1319. https://doi.org/10.3390/s16081319.Search in Google Scholar PubMed PubMed Central
38. Tuncer, T, Dogan, S, Abdar, M, Pławiak, P. A novel facial image recognition method based on perceptual hash using quintet triple binary pattern. Multimed Tool Appl 2020;79:29573–93. https://doi.org/10.1007/s11042-020-09439-8.Search in Google Scholar
39. Kłeczek, P, Dyduch, G, Jaworek-Korjakowska, J, Tadeusiewicz, R. Automated epidermis segmentation in histopathological images of human skin stained with hematoxylin and eosin. In: Gurcan, MN, Tomaszewski, JE, editors. Medical imaging 2017: digital pathology. Orlando, Florida, United States: SPIE Medical Imaging; 2017:101400M p.10.1117/12.2249018Search in Google Scholar
40. Tadeusiewicz, R, Ogiela, M. Picture languages in automatic radiological palm interpretation. Int J Appl Math Comput Sci 2005;15:305–12.Search in Google Scholar
41. Jaworek-Korjakowska, J, Tadeusiewicz, R. Determination of border irregularity in dermoscopic color images of pigmented skin lesions. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society. Chicago, IL, USA: IEEE; 2014:6459–62 pp.10.1109/EMBC.2014.6945107Search in Google Scholar PubMed
42. Prusak, Z, Tadeusiewicz, R, Jastrzębski, R, Jastrzębska, I. Advances and perspectives in using medical informatics for steering surgical robots in welding and training of welders applying long-distance communication links. Weld Technol Rev 2020;92:37–49. https://doi.org/10.26628/wtr.v92i5.1116.Search in Google Scholar
43. Drapikowski, P. Surface modeling—uncertainty estimation and visualization. Comput Med Imaging Graph 2008;32:134–9. https://doi.org/10.1016/j.compmedimag.2007.10.006.Search in Google Scholar PubMed
44. Pławiak, P, Tadeusiewicz, R. Approximation of phenol concentration using novel hybrid computational intelligence methods. Int J Appl Math Comput Sci 2014;24:165–81. https://doi.org/10.2478/amcs-2014-0013.Search in Google Scholar
45. Ogiela, M, Tadeusiewicz, R. Modern computational intelligence methods for the interpretation of medical images. Berlin, Heidelberg: Springer-Verlag; 2008, 84.10.1007/978-3-540-75402-2Search in Google Scholar
46. Jabłoński, M, Tadeusiewicz, R. Vision-based detection of events using line-scan camera. In: 2016 Second international conference on event-based control, communication, and signal processing (EBCCSP). Kraków, Poland: IEEE; 2016:1–3 pp.10.1109/EBCCSP.2016.7605275Search in Google Scholar
47. Ogiela, MR, Tadeusiewicz, R. Image understanding methods in biomedical informatics and digital imaging. J Biomed Inf 2001;34:377–86. https://doi.org/10.1006/jbin.2002.1034.Search in Google Scholar PubMed
48. Drapikowski, P, Nowakowski, T. 3D object modelling in mobile robot environment using B-spline surfaces. In: Proceedings first international symposium on 3D data processing visualization and transmission. Padua, Italy: IEEE; 2002:676–9 pp.10.1109/TDPVT.2002.1024139Search in Google Scholar
49. Drapikowski, P, Kazimierczak-Grygiel, E, Korecki, D, Wiland-Szymańska, J. Verification of geometric model-based plant phenotyping methods for studies of xerophytic plants. Sensors 2016;16:924. https://doi.org/10.3390/s16070924.Search in Google Scholar PubMed PubMed Central
50. Tadeusiewicz, R, Śmietański, J. Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. Kraków, STN, editor, 2011.Search in Google Scholar
51. Cios, KJ, Mamitsuka, H, Nagashima, T, Tadeusiewicz, R. Computational intelligence in solving bioinformatics problems. Artif Intell Med 2005;35:1–8. https://doi.org/10.1016/j.artmed.2005.07.001.Search in Google Scholar PubMed
52. Jaworek-Korjakowska, J, Kłeczek, P, Tadeusiewicz, R. Detection and classification of pigment network in dermoscopic color images as one of the 7-point checklist criteria. In: Advances in intelligent systems and computing. Cham: Springer; 2018, vol 647:174–81 pp.10.1007/978-3-319-66905-2_15Search in Google Scholar
53. Rzecki, K, Pławiak, P, Niedźwiecki, M, Sośnicki, T, Leśkow, J, Ciesielski, M. Person recognition based on touch screen gestures using computational intelligence methods. Inf Sci 2017;415–416:70–84. https://doi.org/10.1016/j.ins.2017.05.041.Search in Google Scholar
54. Zomorodi‐Moghadam, M, Abdar, M, Davarzani, Z, Zhou, X, Pławiak, P, Acharya, UR. Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease. Expert Syst 2019:e12485.10.1111/exsy.12485Search in Google Scholar
55. Jabłoński, M, Tadeusiewicz, R, Piłat, A, Walczyk, J, Tylek, P, Szczepaniak, J, et al.. Vision-based assessment of viability of acorns using sections of their cotyledons during automated scarification procedure. Bio Algorithm Med Syst 2018;14:1–8. https://doi.org/10.1515/bams-2018-0006.Search in Google Scholar
56. Tadeusiewicz, R, Ogiela, M, Szczepaniak, P. Notes on a linguistic description as the basis for automatic image understanding. Int J Appl Math Comput Sci 2009;19:143–50. https://doi.org/10.2478/v10006-009-0013-7.Search in Google Scholar
57. Albu, AB, Beugeling, T, Laurendeau, D. A morphology-based approach for interslice interpolation of anatomical slices from volumetric images. IEEE Trans Biomed Eng 2008;55:2022–38. https://doi.org/10.1109/tbme.2008.921158.Search in Google Scholar
58. Kłeczek, P, Lech, M, Jaworek-Korjakowska, J, Dyduch, G, Tadeusiewicz, R. Segmentation of black ink and melanin in skin histopathological images. In: Gurcan, MN, Tomaszewski, JE, editors. Medical imaging 2018: digital pathology. Houston, Texas, United States: SPIE Medical Imaging; 2018:45 p.10.1117/12.2292859Search in Google Scholar
59. Ali, T, Masood, K, Irfan, M, Draz, U, Nagra, AA, Asif, M, et al.. Multistage segmentation of prostate cancer tissues using sample entropy texture analysis. Entropy 2020;22:1370. https://doi.org/10.3390/e22121370.Search in Google Scholar PubMed PubMed Central
60. Masala, GL, Golosio, B, Oliva, P. An improved Marching Cube algorithm for 3D data segmentation. Comput Phys Commun 2013;184:777–82. https://doi.org/10.1016/j.cpc.2012.09.030.Search in Google Scholar
61. Custodio, L, Pesco, S, Silva, C. An extended triangulation to the Marching Cubes 33 algorithm. J Braz Comput Soc 2019;25:6. https://doi.org/10.1186/s13173-019-0086-6.Search in Google Scholar
62. Wang, J, Huang, Z, Yang, X, Jia, W, Zhou, T. Three-dimensional reconstruction of jaw and dentition CBCT images based on improved marching cubes algorithm. Procedia CIRP 2020;89:239–44. https://doi.org/10.1016/j.procir.2020.05.148.Search in Google Scholar
63. Hu, L, Shi, K, Xu, S, Liu, X. Improved marching cubes algorithm for 3D reconstruction. Chin J Med Imaging Technol 2019;6:925–9.Search in Google Scholar
64. Lorensen, WE, Cline, HE. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput Graph 1987;21:163–9. https://doi.org/10.1145/37402.37422.Search in Google Scholar
Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/bams-2021-0063).
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