TY  - JOUR
T1  - Identification of Rice Varieties Using Machine Learning Algorithms
AU  - Çınar, İlkay
AU  - Koklu, Murat
PY  - 2022
DA  - April
Y2  - 2021
DO  - 10.15832/ankutbd.862482
JF  - Journal of Agricultural Sciences
JO  - J Agr Sci-Tarim Bili
PB  - Ankara University
WT  - DergiPark
SN  - 1300-7580
SP  - 307
EP  - 325
VL  - 28
IS  - 2
LA  - en
AB  - 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.
KW  - Color features
KW  - image processing
KW  - morphological features
KW  - rice classification
KW  - shape features
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - Demirbas, H. and I. Dursun, Determination of some physical properties of wheat grains by using image analysis. Journal of Agricultural Sciences, 2007.
CR  - 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
CR  - 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
CR  - 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
CR  - Silva, C.S. and U. Sonnadara, Classification of rice grains using neural networks. 2013.
CR  - 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.
CR  - 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
CR  - 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).
CR  - 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
CR  - 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
CR  - 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
CR  - Ikegami. 2020  Accessed: 14 May 2020]; Available from: https://www.ikegami.com.
CR  - 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.
CR  - 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.
CR  - 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.
CR  - 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
CR  - Çataloluk, H., Gerçek tıbbi veriler üzerinde veri madenciliği yöntemlerini kullanarak hastalık teşhisi. 2012, Bilecik Üniversitesi, Fen Bilimleri Enstitüsü.
CR  - 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
CR  - 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
CR  - 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
CR  - 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.
CR  - Browne, M.W., Cross-validation methods. Journal of mathematical psychology, 2000. 44(1): p. 108-132. Doi: 10.1006/jmps.1999.1279
CR  - Berrar, D., Cross-validation. Encyclopedia of Bioinformatics Computational Biology, 2019. 1: p. 542-545. Doi: 0.1016/B978-0-12-809633-8.20349-X
CR  - Kılıc, S., Kappa Testi. Journal of Mood Disorders, 2015. 5(3). Doi: 10.5455/jmood.20150920115439
CR  - Landis, J.R. and G.G. Koch, The measurement of observer agreement for categorical data. Biometrics, 1977: p. 159-174. Doi: 10.2307/2529310
CR  - Cinar, I., Yapay Zeka Teknikleri Kullanılarak Pirinç Çeşitlerinin Sınıflandırılması, in Computer Engineering. 2019, Selcuk University: Konya. p. 123.
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - 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
CR  - Arora, R., Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications, 2012. 54(13).
CR  - 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
CR  - Abhang, P.A., B.W. Gawali, and S.C. Mehrotra, Introduction to EEG-and speech-based emotion recognition. 2016: Academic Press.
CR  - 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
UR  - https://doi.org/10.15832/ankutbd.862482
L1  - https://dergipark.org.tr/en/download/article-file/1513632
ER  -