Abstract
Traditional rice grain classification is costly, time-consuming and requires sophisticated human expertise. Besides, computer vision based methods are still based on predefined morphological features that are often not transferable across different types of grains. In this paper, the feasibility of automated feature extraction for rice grain purity analysis has been demonstrated using a Convolutional Neural Network (CNN) based deep learning approach. Due to the lack of benchmark datasets, the paper defines a dataset with technician-verified, labeled images of different types of rice grains with a background of uniform illumination. Moreover, the paper also proposes the architecture of a CNN for automated rice grain feature extraction. The performance of a classifier trained on these features is compared to classifiers trained on morphological features used by modern computer vision approaches. It is found that in this dataset, the proposed method can detect the presence of native and foreign grains in a given sample of rice grains with superior accuracy which is at least 25% better in case of a multiclass classification scenario.
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References
Das, H., Naik, B., Behera, H.S.: Classification of diabetes mellitus disease (DMD): a data mining (DM) approach. In: Progress in Computing, Analytics and Networking, pp. 539–549. Springer, Singapore (2018)
Sahani, R., Rout, C., Badajena, J.C., Jena, A.K., Das, H.: Classification of intrusion detection using data mining techniques. In: Progress in Computing, Analytics and Networking, pp. 753–764. Springer, Singapore (2018)
Qing, Y., Jianhua, C., Zexin, G., Chengxiao, S., Zhiwei, Z.: Inspection of rice appearance quality using machine vision. In: 2010 Second WRI Global Congress on Intelligent Systems, vol. 4, pp. 274–279 (2009). https://doi.org/10.1109/gcis.2009.91
Gayathri Devi, T., Neelamegam, P., Sudha, S.: Machine vision based quality analysis of rice grains, pp. 1052–1055 (2017). https://doi.org/10.1109/icpcsi.2017.8391871
Mahale, B., Korde, S.: Rice quality analysis using image processing techniques. In: International Conference for Convergence for Technology, pp. 1–5. IEEE (2014). https://doi.org/10.1109/i2ct.2014.7092300
Ali, S.F., Jamil, H., Jamil, R., Torij, I., Naz, S.: Low cost solution for rice quality analysis using morphological parameters and its comparison with standard measurements. In: 2017 International Multi-topic Conference (INMIC), pp. 1–6. IEEE, (2017). https://doi.org/10.1109/inmic.2017.8289475
Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A.: Qualitative classification of milled rice grains using computer vision and metaheuristic techniques. J. Food Sci. Technol. 53(1), 118–131 (2016)
Kolkure, V.S., Shaikh, B.N.: Identification and quality testing of rice grains using image processing and neural network. Int. J. Recent Trends Eng. Res. 3(01), 130–135 (2017)
Mousavirad, S.J., Tab, F.A., Mollazade, K.: Design of an expert system for rice kernel identification using optimal morphological features and back propagation neural network. Int. J. Appl. Inf. Syst. 3(2), 33–37 (2012)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986). https://doi.org/10.1109/tpami.1986.4767851
Kirkos, E., Charalambos, S., Yannis, M.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)
Pal, B., Hasan, M.A.M.: Neural network & genetic algorithm based approach to network intrusion detection & comparative analysis of performance. In: 2012 15th International Conference on Computer and Information Technology (ICCIT), pp. 150–154. IEEE (2012)
Pal, B., Ahmed, B.: A deep domain adaption approach for object recognition using Multiple Model Consistency analysis. In : 2016 9th International Conference on Electrical and Computer Engineering (ICECE), pp. 562–565. IEEE (2016)
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Shamim, M.I., Pal, B., Arora, A.S., Pial, M.A. (2020). A Deep Convolutional Neural Network Approach to Rice Grain Purity Analysis. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_19
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DOI: https://doi.org/10.1007/978-981-15-2414-1_19
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