Determination of the mass fraction of granular mixture components by means of computer image analysis
Piotr Zapotoczny
WNT UWM w OlsztynieAbstrakt
Celem pracy było określenie zależności statystycznej pomiędzy gęstością, objętością i masą a wybranymi wielkościami geometrycznymi ziaren ryżu. Zbudowanie efektywnych modeli statystycznych pozwoli na oszacowanie udziału masowego poszczególnych frakcji w mieszaninie na podstawie obrazu 2D. Szybka, tania i skuteczna ocena jakości ziarna ryżu jest kluczowa dla firm zajmujących się obrotem zbożem. W pracy wykorzystano piknometr gazowy do pomiaru objętości ziarna, natomiast do określenia cech geometrycznych wykorzystano skaner płaski i specjalistyczne oprogramowanie. Wyznaczono ponad 70 parametrów geometrycznych, z których wyodrębniono dla ziarna pełnego - Rb, W5, Nv, LminE, ziarna łamanego - Lsz, LminE, Maver, Uw. Współczynniki korelacji wahały się od 0,895 do 0,995 przy poziomie istotności p<. 0,007
Słowa kluczowe:
rice, segmentation, flatbed scanner, correlation, physical parametersBibliografia
Alfaresi A.Y., Hernaman I., Saefulhadjar D. 2024. Bulk density and compact bulk density of rice bran from various regencies mixed with rice husk. Majalah Ilmiah Peternakan, 26(3): 198. https://doi.org/10.24843/mip.2023.v26.i03.p10 Google Scholar
Bhattacharya K.R. 2011. Rice quality: A guide to rice properties and analysis. Woodhead Publishing, Sawston. Google Scholar
Chen Y., Nasrabadi N.M., Tran T.D. 2011. Hyperspectral image classification using dictionary – based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10): 3973-3985. Google Scholar
Dalen G. van. 2004. Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Research International, 37(1): 51-58. Google Scholar
Dalen G. van. 2005. Characterisation of rice using flatbed scanning and image analysis. In: A.P. Riley (Ed.), Food policy, control and research. Chapter 6, p. 149-186. Nova Publishers, Hauppauge. Google Scholar
Dudhrejia M.N. 2017. Grain quality analysis using image processing approach. Ph.D. Synopsis. Submitted to Gujarat Technological University for the award of Ph.D. degree in Computer Engineering. Retrieved from https://www.gtu.ac.in/uploads/119997493005_Mihir_Dudhrejia_Synopsis%20-%202.4.pdf Google Scholar
Emadzadeh B., Razavi S., Farahmandfar R. 2010. Monitoring geometric characteristics of rice during processing by image analysis system and micrometer measurement. International Agrophysics. 24, 21-27. Google Scholar
Ghasemi-Varnamkhasti M., Mohtasebi S.S., Siadat M. 2010. Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements. Journal of Food Engineering, 100(3): 377-387. https://doi.org/10.1016/j.jfoodeng.2010.04.032 Google Scholar
Ghosal M.K., Sarangi P. 2020. Studies on some physical properties of groundnut of TMV-2 variety. International Journal of Chemical Studies, 8(1): 1117-1123. https://doi.org/10.22271/CHEMI.2020.V8.I1O.8399 Google Scholar
Horabik J., Molenda M. 2002. Właściwości fizyczne sypkich surowców spożywczych. Zarys katalogu. Acta Agrophysica, 74: 1-90. Google Scholar
Ibrahim S., Zulkifli N.A., Sabri N., Shari A.A., Noordin M.R.M. 2019. Rice grain classification using multi-class support vector machine (SVM). IAES International Journal of Artificial Intelligence, 8(3): 215-220. https://doi.org/10.11591/ijai.v8.i3.pp215-220 Google Scholar
International Rice Research Institute. https://www.irri.org/ Google Scholar
Kaur S., Singh D. 2015. Geometric feature extraction of selected rice grains using image processing techniques. International Journal of Computer Applications, 124(8): 41-46. https://doi.org/10.5120/ijca2015905576 Google Scholar
Kuan Y.N., Goh K.M,. Lim L.L. 2025. Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques. Engineering Applications of Artificial Intelligence, 148: 110401. https://doi.org/10.1016/j.engappai.2025.110401 Google Scholar
Kurade C., Meenu M., Kalra S., Miglani A., Neelapu B.C., Yu Y., Ramaswamy H.S. 2023. An automated image processing module for quality evaluation of milled rice. Foods, 12(6): 1273. https://doi.org/10.3390/foods12061273 Google Scholar
Lurstwut B., Pornpanomchai CH. 2017. Image analysis based on color, shape and texture for rice seed (Oryza sativa L.) germination evaluation. Agriculture and Natural Resources, 51(5): 383-389. Google Scholar
Mahale B., Korde S. 2014. Rice quality analysis using image processing techniques. 2014 International Conference for Convergence of Technology, I2CT 2014, art. no. 7092300. https://doi.org/10.1109/I2CT.2014.7092300 Google Scholar
Miejsca do odwiedzenia. 2025. Place and see. Retrieved from https://placeandsee.com/pl Google Scholar
Mittal S., Dutta M., Issac A. 2019. Non-destructive image processing based system for assessment of rice quality and defects for classification according to inferred commercial value. Measurement, 148: 106969. https://doi.org/10.1016/j.measurement.2019.106969 Google Scholar
Patrício D.I., Rieder R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153: 69-81. https://doi.org/10.1016/j.compag.2018.08.001 Google Scholar
Sandra, Putri R.E., Djoyowasito G., Wijaya S.N. 2020. Effect of moisture content on some physical and mechanical properties of ‘Genjah Arum’ local rice (Oryza sativa L.) variety in Banyuwangi. IOP Conference Series: Earth and Environmental Science, 515. International Conference of Sustainability Agriculture and Biosystem, 12-13 November 2019, West Sumatera Province, Indonesia. https://doi.org/10.1088/1755-1315/515/1/012020 Google Scholar
Sharma R., Kumar M., Alam M.S. 2021. Image processing techniques to estimate weight and morphological parameters for selected wheat refractions. Scientific Reports, 11, art. No. 20953. https://doi.org/10.1038/s41598-021-00081-4 Google Scholar
Singh S.K., Vidyarthi S.K., Tiwari R. 2020. Machine learnt image processing to predict weight and size of rice kernels. Journal of Food Engineering, 274, art. No. 109828. https://doi.org/10.1016/j.jfoodeng.2019.109828 Google Scholar
Srinivasa K., Moir F., Goodyear-Smith F. 2022. The role of online videos in teaching procedural skills in postgraduate medical education: A scoping review. Journal of Surgical Education, 79(5): 1295-1307. https://doi.org/10.1016/j.jsurg.2022.05.009 Google Scholar
Surender M., Mummadi U.K. 2024. Grain quality analysis from the image through the approaches of segmentation. AIP Conference Proceedings, 3007(1). https://doi.org/10.1063/5.0192997 Google Scholar
Venkatesan M., Anbuselvam Y., Elangaimannan R., Karthikeyan P. 2007. Combining ability for yield and physical characters in rice. ORYZA – An International Journal on Rice, 44(4): 296-299. Google Scholar
Vithu P., Moses J.A. 2016. Machine vision system for food grain quality evaluation: A review. Trends in Food Science & Technology, 56: 13-20. https://doi.org/10.1016/j.tifs.2016.07.011 Google Scholar
Wu Y., Yang Z., Wu W., Li X., Tao D. 2018. Deep-rice: deep multi-sensor image recognition for grading rice. 2018 IEEE International Conference on Information and Automation (ICIA), p. 116-120. Wuyishan, China. https://doi.org/10.1109/ICInfA.2018.8812590 Google Scholar
WNT UWM w Olsztynie
