Corn Seeds Identification Based on Shape and Colour Features

Haddad Alwi Yafie, Ema Rachmawati, Esa Prakasa, Amin Nur



Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.


artificial neural network, eccentricity, feature extraction, region of interest

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P. Daskalov, E. Kirilova, and T. Georgieva, ‘Performance of an automatic inspection system for classification of Fusarium Moniliforme damaged corn seeds by image analysis’, MATEC Web Conf., vol. 210, pp. 1–10, 2018, doi: 10.1051/matecconf/201821002014.

E. Prakasa et al., ‘Automatic region-of-interest selection for corn seed grading’, Proc. - 2017 Int. Conf. Comput. Control. Informatics its Appl. Emerg. Trends Comput. Sci. Eng. IC3INA 2017, vol. 2018-Janua, pp. 23–28, 2017, doi: 10.1109/IC3INA.2017.8251734.

K. Kiratiratanapruk and W. Sinthupinyo, ‘Color and texture for corn seed classification by machine vision’, 2011 Int. Symp. Intell. Signal Process. Commun. Syst. "The Decad. Intell. Green Signal Process. Commun. ISPACS 2011, pp. 7–11, 2011, doi: 10.1109/ISPACS.2011.6146100.

P. Moallem, A. Serajoddin, and H. Pourghassem, ‘Computer vision-based apple grading for golden delicious apples based on surface features’, Inf. Process. Agric., vol. 4, no. 1, pp. 33–40, 2017, doi: 10.1016/j.inpa.2016.10.003.

K. Padmavathi, ‘Investigation and monitoring for leaves disease detection and evaluation using image processing’, Int. Res. J. Eng. Sci. Technol. Innov., vol. 1, no. 3, pp. 66–70, 2012.

M. R. Satpute and S. MJagdale, ‘Color, Size, Volume, Shape and Texture Feature Extraction Techniques for Fruits: A Review’, Int. Res. J. Eng. Technol., no. 2010, pp. 2395–56, 2016.

N. Abdellahhalimi, Roukhe, A., Abdenabi, B., & El Barbri, ‘Sorting Dates Fruit Bunches Based on Their Maturity Using Camera Sensor System’, J. Theor. Appl. Inf. Technol., 2013.

M. A. (University of G. Wirth, ‘Shape Analysis & Measurement Shape Analysis & Measurement’, Image Process., pp. 1–49, 2004.

X. Yang, H. Hong, Z. You, and F. Cheng, ‘Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification’, Sensors (Switzerland), vol. 15, no. 7, pp. 15578–15594, 2015, doi: 10.3390/s150715578.

X. Li, B. Dai, H. Sun, and W. Li, ‘Corn classification system based on computer vision’, Symmetry (Basel)., vol. 11, no. 4, 2019, doi: 10.3390/sym11040591.

X. Chen, Y. Xun, W. Li, and J. Zhang, ‘Combining discriminant analysis and neural networks for corn variety identification’, Comput. Electron. Agric., vol. 71, no. SUPPL. 1, pp. 48–53, 2010, doi: 10.1016/j.compag.2009.09.003.

K. Markham, ‘Simple guide to confusion matrix terminology’, Data Sch., 2015.

E. Hari Rachmawanto, G. Rambu Anarqi, D. R. I. Moses Setiadi, and C. Atika Sari, ‘Handwriting Recognition Using Eccentricity and Metric Feature Extraction Based on K-Nearest Neighbors’, Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 411–416, 2018, doi: 10.1109/ISEMANTIC.2018.8549804.

L. Boyd, S., & Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares Book. 2017.

Y. Ji, Q. Zhao, S. Bi, and T. Shen, ‘Apple Grading Method Based on Features of Color and Defect’, Chinese Control Conf. CCC, vol. 2018-July, pp. 5364–5368, 2018, doi: 10.23919/ChiCC.2018.8483825.

M. Jhuria, A. Kumar, and R. Borse, ‘Image processing for smart farming: Detection of disease and fruit grading’, 2013 IEEE 2nd Int. Conf. Image Inf. Process. IEEE ICIIP 2013, pp. 521–526, 2013, doi: 10.1109/ICIIP.2013.6707647.

H. Toylan and H. Kuscu, ‘A real-time apple grading system using multicolor space’, Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/292681.

A. Mizushima and R. Lu, ‘An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method’, Comput. Electron. Agric., vol. 94, pp. 29–37, 2013, doi: 10.1016/j.compag.2013.02.009.

M. P. Arakeri and Lakshmana, ‘Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry’, Procedia Comput. Sci., vol. 79, pp. 426–433, 2016, doi: 10.1016/j.procs.2016.03.055.

Y. Liu, A. Ouyang, J. Wu, and Y. Ying, ‘An automatic method for identifying different variety of rice seeds using machine vision technology’, Opt. Sensors Sens. Syst. Nat. Resour. Food Saf. Qual., vol. 5996, no. Icnc, p. 59961H, 2005, doi: 10.1117/12.631004.

F. F. Zain and Y. Sibaroni, ‘Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification’, Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 5, no. 2, pp. 108–114, 2019, doi: 10.23917/khif.v5i2.7770.

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