Research Article
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Identification of Rice Varieties Using Machine Learning Algorithms

Year 2022, Volume 28, Issue 2, 307 - 325, 25.04.2022
https://doi.org/10.15832/ankutbd.862482

Abstract

Rice, which has the highest production and consumption rates worldwide, is among the main nutrients in terms of being economical and nutritious in our country as well. Rice goes through some stages of production from the field to the dinner tables. The cleaning phase is the separation of rice from unwanted materials. During the classification phase, solid ones and broken ones are separated and calibration operations are performed. Finally, in the process of extraction based on color features, the striped and stained ones other than the whiteness on the surface of the rice grain are separated. In this paper, five different varieties of rice belonging to the same trademark were selected to carry out classification operations using morphological, shape and color features. A total of 75,000 rice grain images, including 15,000 for each varieties, were obtained. The images were pre-processed using MATLAB software and prepared for feature extraction. Using a combination of 12 morphological, 4 shape features and 90 color features obtained from five different color spaces, a total of 106 features were extracted from the images. For classification, models were created with algorithms using machine learning techniques of k-nearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. With these models, performance measurement values were obtained for feature sets of 12, 16, 90 and 106. Among the models, the success of the algorithms with the highest average classification accuracy was achieved 97.99% with random forest for morphological features. 98.04% were obtained with random forest for morphological and shape features. It was achieved with logistic regression as 99.25% for color features. Finally, 99.91% was obtained with multilayer perceptron for morphological, shape and color features. When the results are examined, it is observed that with the addition of each new feature, the success of classification increases. Based on the performance measurement values obtained, it is possible to say that the study achieved success in classifying rice varieties.

References

  • Tipi, T., et al., Measuring the technical efficiency and determinants of efficiency of rice (Oryza sativa) farms in Marmara region, Turkey. New Zealand Journal of Crop Horticultural Science, 2009. 37(2): p. 121-129. Doi: 10.1080/01140670909510257
  • Yadav, B. and V. Jindal, Monitoring milling quality of rice by image analysis. Computers Electronics in Agriculture, 2001. 33(1): p. 19-33. Doi: 10.1016/S0168-1699(01)00169-7
  • Visen, N.S., et al. Image analysis of bulk grain samples using neural networks. in 2003 ASAE Annual Meeting. 2003. American Society of Agricultural and Biological Engineers. Doi: 10.13031/2013.15002
  • Dubey, B., et al., Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosystems engineering, 2006. 95(1): p. 61-67. Doi: 0.1016/j.biosystemseng.2006.06.001
  • Demirbas, H. and I. Dursun, Determination of some physical properties of wheat grains by using image analysis. Journal of Agricultural Sciences, 2007.
  • Zapotoczny, P., M. Zielinska, and Z. Nita, Application of image analysis for the varietal classification of barley:: Morphological features. Journal of Cereal Science, 2008. 48(1): p. 104-110. Doi: 10.1016/j.jcs.2007.08.006
  • Aggarwal, A.K. and R. Mohan, Aspect ratio analysis using image processing for rice grain quality. International Journal of Food Engineering, 2010. 6(5). Doi: 10.2202/1556-3758.1788
  • OuYang, A.-G., et al. An automatic method for identifying different variety of rice seeds using machine vision technology. in 2010 Sixth International Conference on Natural Computation. 2010. IEEE. Doi: 10.1109/ICNC.2010.5583370
  • Silva, C.S. and U. Sonnadara, Classification of rice grains using neural networks. 2013.
  • Kaur, H. and B. Singh, Classification and grading rice using multi-class SVM. International Journal of Scientific Research Publications, 2013. 3(4): p. 1-5.
  • Abirami, S., P. Neelamegam, and K.H. Thanjavur India, Analysis of Rice Granules using Image Processing and Neural Network Pattern Recognition Tool. 2014. Doi: 10.1.1.673.5557
  • Sethy, P.K. and A. Chatterjee, Rice Variety Identification of Western Odisha Based on Geometrical and Texture Feature. International Journal of Applied Engineering Research, 2018. 13(4).
  • Chen, S., et al., Colored rice quality inspection system using machine vision. Journal of cereal science, 2019. 88: p. 87-95. Doi: 10.1016/j.jcs.2019.05.010
  • Abbaspour-Gilandeh, Y., et al., A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars. Agronomy, 2020. 10(1): p. 117. Doi: 10.3390/agronomy10010117
  • Koklu, M. and I.A. Ozkan, Multiclass classification of dry beans using computer vision and machine learning techniques. Computers Electronics in Agriculture, 2020. 174: p. 105507. Doi: 10.1016/j.compag.2020.105507
  • Ikegami. 2020 Accessed: 14 May 2020]; Available from: https://www.ikegami.com.
  • Pazoki, A., F. Farokhi, and Z. Pazoki, Classification of rice grain varieties using two Artificial Neural Networks (MLP and Neuro-Fuzzy). The Journal of Animal Plant Sciences, 2014. 24(1): p. 336-343.
  • Chaudhary, P., et al., Color transform based approach for disease spot detection on plant leaf. International journal of computer science telecommunications, 2012. 3(6): p. 65-70.
  • Arefi, A., A.M. Motlagh, and R.F. Teimourlou, Wheat class identification using computer vision system and artificial neural networks. International Agrophysics, 2011. 25(4): p. 319-325.
  • Kaya, E. and I. Saritas, Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Computers Electronics in Agriculture, 2019. 166: p. 105016. Doi: 10.1016/j.compag.2019.105016
  • Çataloluk, H., Gerçek tıbbi veriler üzerinde veri madenciliği yöntemlerini kullanarak hastalık teşhisi. 2012, Bilecik Üniversitesi, Fen Bilimleri Enstitüsü.
  • Cinar, I. and M. Koklu, Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems Applications in Engineering, 2019. 7(3): p. 188-194. Doi: 10.18201/ijisae.2019355381
  • Hossin, M. and M. Sulaiman, A review on evaluation metrics for data classification evaluations. International Journal of Data Mining Knowledge Management Process, 2015. 5(2): p. 1. Doi: 10.5121/ijdkp.2015.5201
  • Sokolova, M. and G. Lapalme, A systematic analysis of performance measures for classification tasks. Information processing management, 2009. 45(4): p. 427-437. Doi: 10.1016/j.ipm.2009.03.002
  • Singh, G. and R.K. Panda, Daily sediment yield modeling with artificial neural network using 10-fold cross validation method: a small agricultural watershed, Kapgari, India. International Journal of Earth Sciences Engineering, 2011. 4(6): p. 443-450.
  • Browne, M.W., Cross-validation methods. Journal of mathematical psychology, 2000. 44(1): p. 108-132. Doi: 10.1006/jmps.1999.1279
  • Berrar, D., Cross-validation. Encyclopedia of Bioinformatics Computational Biology, 2019. 1: p. 542-545. Doi: 0.1016/B978-0-12-809633-8.20349-X
  • Kılıc, S., Kappa Testi. Journal of Mood Disorders, 2015. 5(3). Doi: 10.5455/jmood.20150920115439
  • Landis, J.R. and G.G. Koch, The measurement of observer agreement for categorical data. Biometrics, 1977: p. 159-174. Doi: 10.2307/2529310
  • Cinar, I., Yapay Zeka Teknikleri Kullanılarak Pirinç Çeşitlerinin Sınıflandırılması, in Computer Engineering. 2019, Selcuk University: Konya. p. 123.
  • Kumar, B.A., et al., Real time bus travel time prediction using k-NN classifier. Transportation Letters, 2019. 11(7): p. 362-372. Doi: 10.1080/19427867.2017.1366120
  • Beyaz, A. and Ozturk, R., Identification of olive cultivars using image processing techniques. Turkish Journal of Agriculture Forestry, 2016. 40(5): p. 671-683. Doi: 10.3906/tar-1504-95
  • Richman, J.S., Multivariate neighborhood sample entropy: a method for data reduction and prediction of complex data, in Methods in enzymology. 2011, Elsevier. p. 397-408. Doi: 10.1016/B978-0-12-381270-4.00013-5
  • Safavian, S.R. and D. Landgrebe, A survey of decision tree classifier methodology. IEEE transactions on systems, man, cybernetics, 1991. 21(3): p. 660-674. Doi: 10.1109/21.97458
  • Amor, N.B., S. Benferhat, and Z. Elouedi, Qualitative classification with possibilistic decision trees, in Modern Information Processing. 2006, Elsevier. p. 159-169. Doi: 10.1016/B978-044452075-3/50014-5
  • Cruyff, M.J., et al., A review of regression procedures for randomized response data, including univariate and multivariate logistic regression, the proportional odds model and item response model, and self-protective responses, in Handbook of Statistics. 2016, Elsevier. p. 287-315. Doi: 10.1016/bs.host.2016.01.016
  • Kalantar, B., et al., Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards Risk, 2018. 9(1): p. 49-69. Doi: 10.1080/19475705.2017.1407368
  • Sabanci, K., Different apple varieties classification using kNN and MLP algorithms. International Journal of Intelligent Systems Applications in Engineering, 2016: p. 166-169. Doi: 10.18201/ijisae.2016Special%20Issue-146967
  • Arora, R., Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 2012. 54(13).
  • Oshiro, T.M., P.S. Perez, and J.A. Baranauskas. How many trees in a random forest? in International workshop on machine learning and data mining in pattern recognition. 2012. Springer. Doi: 10.1007/978-3-642-31537-4_13
  • Abhang, P.A., B.W. Gawali, and S.C. Mehrotra, Introduction to EEG-and speech-based emotion recognition. 2016: Academic Press.
  • Shi, L., et al., The research of support vector machine in agricultural data classification. International Conference on Computer and Computing Technologies in Agriculture. 2011, Berlin, Heidelberg: Springer. Doi: 10.1007/978-3-642-27275-2_29

Year 2022, Volume 28, Issue 2, 307 - 325, 25.04.2022
https://doi.org/10.15832/ankutbd.862482

Abstract

References

  • Tipi, T., et al., Measuring the technical efficiency and determinants of efficiency of rice (Oryza sativa) farms in Marmara region, Turkey. New Zealand Journal of Crop Horticultural Science, 2009. 37(2): p. 121-129. Doi: 10.1080/01140670909510257
  • Yadav, B. and V. Jindal, Monitoring milling quality of rice by image analysis. Computers Electronics in Agriculture, 2001. 33(1): p. 19-33. Doi: 10.1016/S0168-1699(01)00169-7
  • Visen, N.S., et al. Image analysis of bulk grain samples using neural networks. in 2003 ASAE Annual Meeting. 2003. American Society of Agricultural and Biological Engineers. Doi: 10.13031/2013.15002
  • Dubey, B., et al., Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosystems engineering, 2006. 95(1): p. 61-67. Doi: 0.1016/j.biosystemseng.2006.06.001
  • Demirbas, H. and I. Dursun, Determination of some physical properties of wheat grains by using image analysis. Journal of Agricultural Sciences, 2007.
  • Zapotoczny, P., M. Zielinska, and Z. Nita, Application of image analysis for the varietal classification of barley:: Morphological features. Journal of Cereal Science, 2008. 48(1): p. 104-110. Doi: 10.1016/j.jcs.2007.08.006
  • Aggarwal, A.K. and R. Mohan, Aspect ratio analysis using image processing for rice grain quality. International Journal of Food Engineering, 2010. 6(5). Doi: 10.2202/1556-3758.1788
  • OuYang, A.-G., et al. An automatic method for identifying different variety of rice seeds using machine vision technology. in 2010 Sixth International Conference on Natural Computation. 2010. IEEE. Doi: 10.1109/ICNC.2010.5583370
  • Silva, C.S. and U. Sonnadara, Classification of rice grains using neural networks. 2013.
  • Kaur, H. and B. Singh, Classification and grading rice using multi-class SVM. International Journal of Scientific Research Publications, 2013. 3(4): p. 1-5.
  • Abirami, S., P. Neelamegam, and K.H. Thanjavur India, Analysis of Rice Granules using Image Processing and Neural Network Pattern Recognition Tool. 2014. Doi: 10.1.1.673.5557
  • Sethy, P.K. and A. Chatterjee, Rice Variety Identification of Western Odisha Based on Geometrical and Texture Feature. International Journal of Applied Engineering Research, 2018. 13(4).
  • Chen, S., et al., Colored rice quality inspection system using machine vision. Journal of cereal science, 2019. 88: p. 87-95. Doi: 10.1016/j.jcs.2019.05.010
  • Abbaspour-Gilandeh, Y., et al., A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars. Agronomy, 2020. 10(1): p. 117. Doi: 10.3390/agronomy10010117
  • Koklu, M. and I.A. Ozkan, Multiclass classification of dry beans using computer vision and machine learning techniques. Computers Electronics in Agriculture, 2020. 174: p. 105507. Doi: 10.1016/j.compag.2020.105507
  • Ikegami. 2020 Accessed: 14 May 2020]; Available from: https://www.ikegami.com.
  • Pazoki, A., F. Farokhi, and Z. Pazoki, Classification of rice grain varieties using two Artificial Neural Networks (MLP and Neuro-Fuzzy). The Journal of Animal Plant Sciences, 2014. 24(1): p. 336-343.
  • Chaudhary, P., et al., Color transform based approach for disease spot detection on plant leaf. International journal of computer science telecommunications, 2012. 3(6): p. 65-70.
  • Arefi, A., A.M. Motlagh, and R.F. Teimourlou, Wheat class identification using computer vision system and artificial neural networks. International Agrophysics, 2011. 25(4): p. 319-325.
  • Kaya, E. and I. Saritas, Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features. Computers Electronics in Agriculture, 2019. 166: p. 105016. Doi: 10.1016/j.compag.2019.105016
  • Çataloluk, H., Gerçek tıbbi veriler üzerinde veri madenciliği yöntemlerini kullanarak hastalık teşhisi. 2012, Bilecik Üniversitesi, Fen Bilimleri Enstitüsü.
  • Cinar, I. and M. Koklu, Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems Applications in Engineering, 2019. 7(3): p. 188-194. Doi: 10.18201/ijisae.2019355381
  • Hossin, M. and M. Sulaiman, A review on evaluation metrics for data classification evaluations. International Journal of Data Mining Knowledge Management Process, 2015. 5(2): p. 1. Doi: 10.5121/ijdkp.2015.5201
  • Sokolova, M. and G. Lapalme, A systematic analysis of performance measures for classification tasks. Information processing management, 2009. 45(4): p. 427-437. Doi: 10.1016/j.ipm.2009.03.002
  • Singh, G. and R.K. Panda, Daily sediment yield modeling with artificial neural network using 10-fold cross validation method: a small agricultural watershed, Kapgari, India. International Journal of Earth Sciences Engineering, 2011. 4(6): p. 443-450.
  • Browne, M.W., Cross-validation methods. Journal of mathematical psychology, 2000. 44(1): p. 108-132. Doi: 10.1006/jmps.1999.1279
  • Berrar, D., Cross-validation. Encyclopedia of Bioinformatics Computational Biology, 2019. 1: p. 542-545. Doi: 0.1016/B978-0-12-809633-8.20349-X
  • Kılıc, S., Kappa Testi. Journal of Mood Disorders, 2015. 5(3). Doi: 10.5455/jmood.20150920115439
  • Landis, J.R. and G.G. Koch, The measurement of observer agreement for categorical data. Biometrics, 1977: p. 159-174. Doi: 10.2307/2529310
  • Cinar, I., Yapay Zeka Teknikleri Kullanılarak Pirinç Çeşitlerinin Sınıflandırılması, in Computer Engineering. 2019, Selcuk University: Konya. p. 123.
  • Kumar, B.A., et al., Real time bus travel time prediction using k-NN classifier. Transportation Letters, 2019. 11(7): p. 362-372. Doi: 10.1080/19427867.2017.1366120
  • Beyaz, A. and Ozturk, R., Identification of olive cultivars using image processing techniques. Turkish Journal of Agriculture Forestry, 2016. 40(5): p. 671-683. Doi: 10.3906/tar-1504-95
  • Richman, J.S., Multivariate neighborhood sample entropy: a method for data reduction and prediction of complex data, in Methods in enzymology. 2011, Elsevier. p. 397-408. Doi: 10.1016/B978-0-12-381270-4.00013-5
  • Safavian, S.R. and D. Landgrebe, A survey of decision tree classifier methodology. IEEE transactions on systems, man, cybernetics, 1991. 21(3): p. 660-674. Doi: 10.1109/21.97458
  • Amor, N.B., S. Benferhat, and Z. Elouedi, Qualitative classification with possibilistic decision trees, in Modern Information Processing. 2006, Elsevier. p. 159-169. Doi: 10.1016/B978-044452075-3/50014-5
  • Cruyff, M.J., et al., A review of regression procedures for randomized response data, including univariate and multivariate logistic regression, the proportional odds model and item response model, and self-protective responses, in Handbook of Statistics. 2016, Elsevier. p. 287-315. Doi: 10.1016/bs.host.2016.01.016
  • Kalantar, B., et al., Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards Risk, 2018. 9(1): p. 49-69. Doi: 10.1080/19475705.2017.1407368
  • Sabanci, K., Different apple varieties classification using kNN and MLP algorithms. International Journal of Intelligent Systems Applications in Engineering, 2016: p. 166-169. Doi: 10.18201/ijisae.2016Special%20Issue-146967
  • Arora, R., Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 2012. 54(13).
  • Oshiro, T.M., P.S. Perez, and J.A. Baranauskas. How many trees in a random forest? in International workshop on machine learning and data mining in pattern recognition. 2012. Springer. Doi: 10.1007/978-3-642-31537-4_13
  • Abhang, P.A., B.W. Gawali, and S.C. Mehrotra, Introduction to EEG-and speech-based emotion recognition. 2016: Academic Press.
  • Shi, L., et al., The research of support vector machine in agricultural data classification. International Conference on Computer and Computing Technologies in Agriculture. 2011, Berlin, Heidelberg: Springer. Doi: 10.1007/978-3-642-27275-2_29

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

İlkay ÇINAR (Primary Author)
SELCUK UNIVERSITY
0000-0003-0611-3316
Türkiye


Murat KOKLU
SELCUK UNIVERSITY
0000-0002-2737-2360
Türkiye

Publication Date April 25, 2022
Application Date January 17, 2021
Acceptance Date May 27, 2021
Published in Issue Year 2022, Volume 28, Issue 2

Cite

Bibtex @research article { ankutbd862482, journal = {Journal of Agricultural Sciences}, issn = {}, eissn = {2148-9297}, address = {}, publisher = {Ankara University}, year = {2022}, volume = {28}, pages = {307 - 325}, doi = {10.15832/ankutbd.862482}, title = {Identification of Rice Varieties Using Machine Learning Algorithms}, key = {cite}, author = {Çınar, İlkay and Koklu, Murat} }
APA Çınar, İ. & Koklu, M. (2022). Identification of Rice Varieties Using Machine Learning Algorithms . Journal of Agricultural Sciences , 28 (2) , 307-325 . DOI: 10.15832/ankutbd.862482
MLA Çınar, İ. , Koklu, M. "Identification of Rice Varieties Using Machine Learning Algorithms" . Journal of Agricultural Sciences 28 (2022 ): 307-325 <https://dergipark.org.tr/en/pub/ankutbd/issue/68966/862482>
Chicago Çınar, İ. , Koklu, M. "Identification of Rice Varieties Using Machine Learning Algorithms". Journal of Agricultural Sciences 28 (2022 ): 307-325
RIS TY - JOUR T1 - Identification of Rice Varieties Using Machine Learning Algorithms AU - İlkay Çınar , Murat Koklu Y1 - 2022 PY - 2022 N1 - doi: 10.15832/ankutbd.862482 DO - 10.15832/ankutbd.862482 T2 - Journal of Agricultural Sciences JF - Journal JO - JOR SP - 307 EP - 325 VL - 28 IS - 2 SN - -2148-9297 M3 - doi: 10.15832/ankutbd.862482 UR - https://doi.org/10.15832/ankutbd.862482 Y2 - 2021 ER -
EndNote %0 Journal of Agricultural Sciences Identification of Rice Varieties Using Machine Learning Algorithms %A İlkay Çınar , Murat Koklu %T Identification of Rice Varieties Using Machine Learning Algorithms %D 2022 %J Journal of Agricultural Sciences %P -2148-9297 %V 28 %N 2 %R doi: 10.15832/ankutbd.862482 %U 10.15832/ankutbd.862482
ISNAD Çınar, İlkay , Koklu, Murat . "Identification of Rice Varieties Using Machine Learning Algorithms". Journal of Agricultural Sciences 28 / 2 (April 2022): 307-325 . https://doi.org/10.15832/ankutbd.862482
AMA Çınar İ. , Koklu M. Identification of Rice Varieties Using Machine Learning Algorithms. J Agr Sci-Tarim Bili. 2022; 28(2): 307-325.
Vancouver Çınar İ. , Koklu M. Identification of Rice Varieties Using Machine Learning Algorithms. Journal of Agricultural Sciences. 2022; 28(2): 307-325.
IEEE İ. Çınar and M. Koklu , "Identification of Rice Varieties Using Machine Learning Algorithms", Journal of Agricultural Sciences, vol. 28, no. 2, pp. 307-325, Apr. 2022, doi:10.15832/ankutbd.862482