Wykorzystanie niezmienników momentowych Hu i momentów Zernike’a do rozpoznawania ziaren jęczmienia dwu- i sześciorzędowych

ADJEMOUT O., HAMMOUCHE K., DIAF M. 2007. Automatic seeds recognition by size, form and texture features. 2007 9th International Symposium on Signal Processing and Its Applications, ISSPA 2007, Proceedings. https://doi.org/10.1109/ISSPA.2007.4555428
Crossref   Google Scholar

ASLI B.H.S., FLUSSER J., ZHAO Y., ERKOYUNCU J.A. 2019. Filter-generating system of Zernike polynomials. Automatica, 108: 108498. https://doi.org/10.1016/j.automatica.2019.108498
Crossref   Google Scholar

BABATUNDE O.H., ARMSTRONG L., LENG J., DIEPEVEEN D. 2014. Zernike Moments and Genetic Algorithm: Tutorial and Application. British Journal of Mathematics & Computer Science, 4(15): 2217–2236. https://doi.org/10.9734/BJMCS/2014/10931
Crossref   Google Scholar

BAIK B.K., ULLRICH S.E. 2008. Barley for food: Characteristics, improvement, and renewed interest. Journal of Cereal Science, 48(2): 233–242. https://doi.org/10.1016/J.JCS.2008.02.002
Crossref   Google Scholar

DOLATA P., REINER J. 2018. Barley variety recognition with viewpoint-aware double-stream convolutional neural networks. Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, p. 101–105. https://doi.org/10.15439/2018F286
Crossref   Google Scholar

DU J.X., ZHAI C.M., WANG Q.P. 2013. Recognition of plant leaf image based on fractal dimension features. Neurocomputing, 116: 150–156. https://doi.org/10.1016/J.NEUCOM.2012.03.028
Crossref   Google Scholar

FITZSIMMONS R.W., WRIGLEY C.W. 1985. Australian Barleys: Identification of Varieties, Grain Defects and Foreign Seeds. CSIRO Publishing, Clayton, Australia.
Crossref   Google Scholar

FLUSSER J., SUK T., ZITOVÁ B. 2009. Moments and Moment Invariants in Pattern Recognition. John Wiley & Sons, Hoboken. https://doi.org/10.1002/9780470684757
Crossref   Google Scholar

GOZUKIRMIZI N., KARLIK E. 2017. Barley (Hordeum vulgare L.) Improvement Past, Present and Future. In: Brewing Technology. Ed. M. Kanauchi. InTech. https://doi.org/10.5772/INTECHOPEN.68359
Crossref   Google Scholar

GRIFFEY C., BROOKS W., KURANTZ M., THOMASON W., TAYLOR F., OBERT D., MOREAU R., FLORES R., SOHN M., HICKS K. 2010. Grain composition of Virginia winter barley and implications for use in feed, food, and biofuels production. Journal of Cereal Science, 51(1): 41–49. https://doi.org/10.1016/J.JCS.2009.09.004
Crossref   Google Scholar

HEBDA T., MICEK P. 2007. Cechy geometryczne ziarna wybranych odmian zbóż. Inżynieria Rolnicza, 5(93).   Google Scholar

HUANG Z., LENG J. 2010. Analysis of Hu’s moment invariants on image scaling and rotation. ICCET 2010–2010 International Conference on Computer Engineering and Technology, Proceedings, 7. https://doi.org/10.1109/ICCET.2010.5485542
Crossref   Google Scholar

KHAIRNAR K., KHAN S. 2022. Plant Leaf Disease Segmentation and Feature Extraction using Image Processing. International Journal of Advance Research and Innovative Ideas in Education, 8(1).   Google Scholar

World Barley production. 2023. Knoema. https://knoema.com/atlas/World/topics/Agriculture/Crops-Production-Quantity-tonnes/Barley-production   Google Scholar

KOZŁOWSKI M., GÓRECKI P., SZCZYPIŃSKI P.M. 2019. Varietal classification of barley by convolutional neural networks. Biosystems Engineering, 184: 155–165. https://doi.org/10.1016/j.biosystemseng.2019.06.012
Crossref   Google Scholar

KURTULMUŞ F., ALIBAŞ İ., KAVDIR I. 2016. Classification of pepper seeds using machine vision based on neural network. International Journal of Agricultural and Biological Engineering, 9(1): 51–62. https://doi.org/10.25165/IJABE.V9I1.1790   Google Scholar

LAMPA P., MRZYGLÓD M., REINER J. 2016. Methods of manipulation and image acquisition of natural products on the example of cereal grains. Control and Cybernetics, 45(3).   Google Scholar

LUCKNER M. 2008. Automatyczna identyfikacja wybranych symboli notacji muzycznej. In: Zastosowania metod statystycznych w badaniach naukowych III, p. 35–43. StatSoft Polska, Kraków.   Google Scholar

LUKIC M., TUBA E., TUBA M. 2017. Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns. SAMI 2017 – IEEE 15th International Symposium on Applied Machine Intelligence and Informatics, Proceedings, p. 485–490. https://doi.org/10.1109/SAMI.2017.7880358
Crossref   Google Scholar

MARCUS J.B. 2013. Chapter 4 – Carbohydrate Basics: Sugars, Starches and Fibers in Foods and Health. Culinary Nutrition. The Science and Practice of Healthy Cooking, p. 149–187. https://doi.org/10.1016/b978-0-12-391882-6.00004-2
Crossref   Google Scholar

MAROUF H., FAEZ K. 2013. Zernike Moment-Based Feature Extraction For Facial Recognition of Identical Twins. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 3(6). https://doi.org/10.5121/ijcseit.2013.3601
Crossref   Google Scholar

MARTIN H.J.A., SANTOS M., DE LOPE J. 2010. Orthogonal variant moments features in image analysis. Information Sciences, 180(6) : 846–860. https://doi.org/10.1016/J.INS.2009.08.032
Crossref   Google Scholar

MISHRA D., MAJHI B., SA P.K. 2017. Improved feature selection for neighbor embedding super-resolution using zernike moments. Advances in Intelligent Systems and Computing, 460 AISC: 13–24. https://doi.org/10.1007/978-981-10-2107-7_2/FIGURES/6
Crossref   Google Scholar

QADRI S., FURQAN QADRI S., HUSNAIN M., SAAD MISSEN M.M., KHAN D.M., MUZAMMIL-UL-REHMAN, RAZZAQ A., ULLAH S. 2019. Machine vision approach for classification of citrus leaves using fused features. International Journal of Food Properties, 22(1), 2071–2088. https://doi.org/10.1080/10942912.2019.1703738
Crossref   Google Scholar

PALLAVI P., VEENA DEVI V.S. 2014. Leaf Recognition Based on Feature Extraction and Zernike Moments. International Journal of Innovative Research in Computer and Communication Engineering, 2(2).   Google Scholar

RAMAGE R.T. 2011. A History of Barley Breeding Methods. Plant Breeding Reviews, 5(4): 95–138. https://doi.org/10.1002/9781118061022.CH4
Crossref   Google Scholar

ROGALSKA U. 2011. Podstawy hodowli jęczmienia. EUREQUA. http://www.uwm.edu.pl/eurequa/pl/I_opr.met.htm   Google Scholar

SABHARA R. 2013. Comparative Study of Hu Moments and Zernike Moments in Object Recognition. The Smart Computing Review, 3(3). https://doi.org/10.6029/smartcr.2013.03.003
Crossref   Google Scholar

SALEEM G., AKHTAR M., AHMED N., QURESHI W.S. 2019. Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157: 270–280. https://doi.org/10.1016/J.COMPAG.2018.12.038
Crossref   Google Scholar

SALVE P., SARDESAI M., MANZA R., YANNAWAR P. 2016. Identification of the Plants Based on Leaf Shape Descriptors. Advances in Intelligent Systems and Computing, 379: 85–101. https://doi.org/10.1007/978-81-322-2517-1_10
Crossref   Google Scholar

SHI Y., LI J., YU Z., LI Y., HU Y., WU L. 2022. Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model. Foods, 11(21). https://doi.org/10.3390/FOODS11213531
Crossref   Google Scholar

SOKOLOVA M., LAPALME G. 2009. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), https://doi.org/10.1016/j.ipm.2009.03.002
Crossref   Google Scholar

SZCZYPIŃSKI P.M., KLEPACZKO A., ZAPOTOCZNY P. 2015. Identifying barley varieties by computer vision. Computers and Electronics in Agriculture, 110: 1–8. https://doi.org/10.1016/j.compag.2014.09.016
Crossref   Google Scholar

SZTURO K. 2023. Integration of an information ontology-based expert system with machine learning methods for barley kernel defects recognition. Lodz University of Technology, Łódź.   Google Scholar

TEAGUE M.R. 1980. Image analysis via the general theory of moments. Journal of the Optical Society of America, 70(8). https://doi.org/10.1364/JOSA.70.000920
Crossref   Google Scholar

TSOLAKIDIS D.G., KOSMOPOULOS D.I., PAPADOURAKIS G. 2014. Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients. Lecture Notes in Computer Science 8445 LNCS. https://doi.org/10.1007/978-3-319-07064-3_33
Crossref   Google Scholar

TYYSTJÄRVI E., NØRREMARK M., MATTILA H., KERÄNEN M., HAKALA-YATKIN M., OTTOSEN C.O., ROSENQVIST E. 2011. Automatic identification of crop and weed species with chlorophyll fluorescence induction curves. Precision Agriculture, 12(4). https://doi.org/10.1007/S11119-010-9201-6/FIGURES/7
Crossref   Google Scholar

WEE C.Y., PARAMESRAN R., TAKEDA F. 2006. Fast computation of zernike moments for rice sorting system. Proceedings – International Conference on Image Processing, ICIP, 6. https://doi.org/10.1109/ICIP.2007.4379547
Crossref   Google Scholar

WEE C.Y., PARAMESRAN R., TAKEDA F. 2009. Sorting of rice grains using Zernike moments. Journal of Real-Time Image Processing, 4(4). https://doi.org/10.1007/S11554-009-0117-1/TABLES/3
Crossref   Google Scholar

WEE C.Y., RAVEENDRAN P., TAKEDA F., TSUZUKI T., KADOTA H., SHIMANOUCHI S. 2002. Feature reduction of Zernike moments using genetic algorithm for neural network classification of rice grain. Proceedings of the International Joint Conference on Neural Networks, 1. https://doi.org/10.1109/IJCNN.2002.1005614
Crossref   Google Scholar

ZAPOTOCZNY P., REINER J., MRZYGŁÓD M., LAMPA P. 2020. The use of polarized light and image analysis in evaluations of the severity of fungal infection in barley grain. Computers and Electronics in Agriculture, 169. https://doi.org/10.1016/J.COMPAG.2019.105154
Crossref   Google Scholar