PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
17--28
Opis fizyczny
Bibliogr. 64 poz., rys.
Twórcy
  • Department of Materials Science, Welding and Strength of Materials, Wrocław University of Science and Technology, Wrocław, Poland
  • Department of Materials Science, Welding and Strength of Materials, Wrocław University of Science and Technology, Wrocław, Poland
  • Department of Mechanics, Materials and Biomedical Engineering, Wrocław University of Science and Technology, Wrocław, Poland
autor
  • Department of Mechanics, Materials and Biomedical Engineering, Wrocław University of Science and Technology, Smoluchowskiego 25, 50-372 Wrocław, Poland
  • Department of Mechanics, Materials and Biomedical Engineering, Wrocław University of Science and Technology, Wrocław, Poland
autor
  • Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Wrocław, Poland
  • Research Centre for Cultivar Testing, Słupia Wielka, Poland
Bibliografia
  • 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.
  • 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.
  • 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.
  • 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.
  • 5. Chen Z, Wassgren C, Ambrose K. A review of grain kernel damage: mechanisms, modeling, and testing procedures. Trans ASABE 2020;63:455-75.
  • 6. Paulsen MR, Nave WR, Mounts TL, Gray LE. Storability of harvestdamaged soybeans. Trans ASAE 1981;24:1583-9.
  • 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.
  • 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.
  • 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.
  • 10. Smith JA. Combine adjustments and operation to minimize damage to dry edible bean seed. St. Joseph, Michigan: American Society of Agricultural Engineers; 1997.
  • 11. Kowalczuk J. Pattern of seed losses and damage during soybean harvest with grain combine harvesters. Int Agrophys 1999;13:103-7.
  • 12. Hermann D. Optimisation of combine harvesters using modelbased control. Lyngby, Denmark: Technical University of Denmark, DTU Elektro; 2018.
  • 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.
  • 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.
  • 15. Zhao Z, Li Y, Chen J, Xu J. Grain separation loss monitoring system in combine harvester. Comput Electron Agric 2011;76:183-8.
  • 16. Bieniek J, Banasiak J, Lewandowski B, Detyna J. Cleanness of the grain obtained on the sectional sieve. Acta Agrophysica 2001;46: 7-14.
  • 17. Myhan R, Jachimczyk E. Grain separation in a straw walker unit of a combine harvester: process model. Biosyst Eng 2016;145:93-107.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 30. Paulsen MR, Nave WR, Gray LE. Soybean seed quality as affected by impact damage. Trans ASAE 1981;24:1577-82.
  • 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.
  • 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.
  • 33. Woźniak W, Styk W. Internal damage to wheat grain as a result of wetting and drying. Dry Technol 1996;14:349-65.
  • 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.
  • 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.
  • 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.
  • 37. Jabłoński M, Tylek P, Walczyk J, Tadeusiewicz R, Piłat A. Colourbased binary discrimination of scarified Quercus robur acorns under varying illumination. Sensors 2016;16:1319.
  • 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.
  • 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.
  • 40. Tadeusiewicz R, Ogiela M. Picture languages in automatic radiological palm interpretation. Int J Appl Math Comput Sci 2005;15:305-12.
  • 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.
  • 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.
  • 43. Drapikowski P. Surface modeling-uncertainty estimation and visualization. Comput Med Imaging Graph 2008;32:134-9.
  • 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.
  • 45. Ogiela M, Tadeusiewicz R. Modern computational intelligence methods for the interpretation of medical images. Berlin, Heidelberg: Springer-Verlag; 2008, 84.
  • 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.
  • 47. Ogiela MR, Tadeusiewicz R. Image understanding methods in biomedical informatics and digital imaging. J Biomed Inf 2001;34: 377-86.
  • 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.
  • 49. Drapikowski P, Kazimierczak-Grygiel E, Korecki D, WilandSzymańska J. Verification of geometric model-based plant phenotyping methods for studies of xerophytic plants. Sensors 2016;16:924.
  • 50. Tadeusiewicz R, Śmietański J. Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. Kraków, STN, editor, 2011.
  • 51. Cios KJ, Mamitsuka H, Nagashima T, Tadeusiewicz R. Computational intelligence in solving bioinformatics problems. Artif Intell Med 2005;35:1-8.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 60. Masala GL, Golosio B, Oliva P. An improved Marching Cube algorithm for 3D data segmentation. Comput Phys Commun 2013; 184:777-82.
  • 61. Custodio L, Pesco S, Silva C. An extended triangulation to the Marching Cubes 33 algorithm. J Braz Comput Soc 2019;25:6.
  • 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.
  • 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.
  • 64. Lorensen WE, Cline HE. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput Graph 1987;21:163-9.
Uwagi
Opublikowane przez De Gruyter. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f2e6ab0e-c705-4ed8-8892-5624ef2e84d1
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.