Exploring Hu’s moment invariants and Zernike moments for effective identification of two-row and six-row barley varieties
Karolina Szturo
University of Warmia and Mazury in OlsztynAbstract
Barley variety identification is a complex and economically significant task. The identification of two-row and six-row grains is particularly important due to their different characteristics, such as protein and starch content, which have specific implications in different applications. This paper evaluates the effectiveness of discriminating between two-row and six-row barley grains using Hu moment invariants and Zernike moments in combination with various classifiers including linear and SVM classifiers with linear, RBF, polynomial, and sigmoid kernels. The application of Zernike moments and an SVM classifier using an RBF kernel achieved an accuracy level of 99.2%. In comparison, the application of Hu’s moment invariants resulted in an accuracy of 98.5%.
Keywords:
Zernike moments, Hu moment invariants, Image analysis, Barley grainSupporting Agencies
References
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
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