An intelligent classification model for peanut’s varieties by color and texture features

  • Authors

    • Narendra V G Computer Scinece and Engineering Dept., Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal-576104, INDIA
    • Anita S. Kini
    • Anita S. Kini
    2018-08-23
    https://doi.org/10.14419/ijet.v7i2.27.12473
  • Peanut Variety Discrimination, Color and Texture Features, Image Processing, Computer Vision, Machine Learning

  • Abstract

    India is the second largest producer of peanuts in the world, and they are available in different forms: Bold or Runner, Java or Spanish and Red Natal. The main peanuts varieties produced in India are Kadiri-2, Kadiri-3, BG-1, BG-2, Kuber, GAUG-1, GAUG-10, PG-1, T-28, T-64, Chandra, Chitra, Kaushal, Parkash, Amber, etc. Peanut is the prime crop for our country’s peasants to increase their agricultural income. However, our country’s international trade price of peanut is only 80% of the average market price. The automation level of testing peanut kernels’ quality is low because of workers fatigue, and most of the work is done by manpower leads costly. The peanuts are evaluated in many areas for sowing and oilseed processing; they must be identified quickly and accurately for selection of a correct variety and kernels’ quality. The proposed testing method based on image processing and computer vision is a new one which is undamaged, speedy with high distinguishing rate, repeatability and low cost and fatigue. In this paper, machine-learning classifiers (Multilayer Perceptron, Simple Logistic, Support Vector Machines, and Sequential Minimal Optimization and Logistic classifiers) are investigated to obtain the best predictive model for peanuts classification. The training and test sets are used to tune the model parameters during the training epochs by varying the complexity of the predictive models with K-fold cross-validation. After obtaining optimized models for each level of complexity, a dedicated validation set is used to validate predictive models. The developed computer vision system provided an overall accuracy rate for the best predictive model in discriminating peanuts variety are Random Forest (82.27%), Multilayer Perceptron(84.9%), and libSVM (86.07%)

     

     

  • References

    1. [1] Burks T. F., Shearer S. A., & Payne F. A. (2000), Classification of weed species using color texture features and discriminant analysis. Transactions of the American Society of Agricultural Engineers, 43(2), 441–448. https://doi.org/10.13031/2013.2723.

      [2] Chen X., Xun Y., Li, W., & Zhang J. (2010), Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture, 71, 48–53. https://doi.org/10.1016/j.compag.2009.09.003 ...

      [3] Clausi D. A. (2002), an analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing, 28, 45–62. https://doi.org/10.5589/m02-004.

      [4] Dubey B P, Bhagwat S G, Shouche S P (2006), Potential of artificial neural networks in varietal identification using morphometry of wheat grains, Bio-system Engineering 95, 61-67. https://doi.org/10.1016/j.biosystemseng.2006.06.001.

      [5] F. Kurtulmus and H. Unal (2015), Discriminating rapeseed varieties using computer vision and machine learning, Expert Systems with Applications, 42, 1880–1891. https://doi.org/10.1016/j.eswa.2014.10.003.

      [6] Han Zhongzhi, Zhao Yougang (2009), A Cultivar Identification and Quality Detection Method of Peanut Based on Appearance Characteristics, Journal of the Chinese Cereals and Oils Association, 24, 123-126.

      [7] HAN Zhong-zhi, LI Yan-zhao, LIU Jing, ZHAO You-gang (2010), Quality Grade-Testing of Peanut Based on Image Processing, Third International Conference on Information and Computing, 333-336, https://doi.org/10.1109/ICIC.2010.270.

      [8] Haralick R. M. (1979). Statistical and structural approaches to texture, Proceedings of the IEEE, 67, 786–804. https://doi.org/10.1109/PROC.1979.11328.

      [9] Keuchel J., Naumann S., Heiler M., & Siegmund A. (2003), Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data, Remote Sensing of Environment, 86(4), 530–541. https://doi.org/10.1016/S0034-4257(03)00130-5.

      [10] Lu Y., Boukharouba K., Boonært J., Fleury A., & Lecoeuche S. (2014), Application of an incremental SVM algorithm for on-line human recognition from video surveillance using texture and color features, Neurocomputing, 126, 132–140. https://doi.org/10.1016/j.neucom.2012.08.071.

      [11] Lu Y., Du C., Yu C., & Zhou J. (2014), Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS), Computers and Electronics in Agriculture, 107, 58–63. https://doi.org/10.1016/j.compag.2014.06.005.

      [12] Mollazade K., Omid M., Tab F. A., Kalaj Y. R., Mohtasebi S. S., & Zude M. (2013), Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging, Computers and Electronics in Agriculture, 98, 34–45. https://doi.org/10.1016/j.compag.2013.07.011.

      [13] Nnorom I. C., Jarzynska, G., Drewnowska M., Dry_załowska A., Kojta A., Pankavec S. (2013), Major and trace elements in sclerotium of Pleurotus tuberregium (Óstu) mushroom—Dietary intake and risk in southeastern Nigeria, Journal of Food Composition and Analysis, 29(1), 73–81. https://doi.org/10.1016/j.jfca.2012.10.001.

      [14] Omid M., Mahmoudi A., & Omid M. H. (2009), an intelligent system for sorting pistachio nut varieties, Expert Systems with Applications, 36(9), 11528–11535. https://doi.org/10.1016/j.eswa.2009.03.040.

      [15] Pattee H.E., and C.Y. Young (1982), Peanut Science and Technology, American Peanut Research and Education Society, Inc. Yoakum, Texas, USA.

      [16] Pazoki A. R., Farokhi F., & Pazoki Z. (2014), Classification of rice grain varieties using two artificial neural networks (mlp and neuro-fuzzy), The Journal of Animal & Plant Sciences, 24(1), 336–343.

      [17] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., (2011), Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.

      [18] Pydipati R., Burks T. F., & Lee W. S. (2006), Identification of citrus disease using color texture features and discriminant analysis, Computers and Electronics in Agriculture, 52(1–2), 49–59. https://doi.org/10.1016/j.compag.2006.01.004.

      [19] Sakai N, Yonekawa S, Matsuzaki A (1996), Two-dimensional image analysis of the shape of rice and its application to separating varieties, Journal of Food Engineering, 27, 397-407. https://doi.org/10.1016/0260-8774(95)00022-4.

      [20] Shearer S. A & Holmes R. G. (1990), Plant identification using color co-occurrence matrices. Transactions of the American Society of Agricultural Engineers, 33(6), 2037–2044.

      [21] Zapotoczny P. (2014), Discrimination of wheat grain varieties using image analysis and multidimensional analysis texture of grain mass, International Journal of Food Properties, 17, 139–151. https://doi.org/10.1080/10942912.2011.615085.

      [22] Zhao Chunming, Han Zhongzhi, Yang Jinzhong (2009), Study on Application of Image Process in Ear Traits for DUS Testing in Maize, Acta Agronomica Sinica, 42, 4100-4105.

      [23] Zou Q., Fang H., Liu F., Kong W., & He Y. (2011), Comparative study of distance discriminant analysis and bp neural network for identification of rapeseed cultivars using visible/near-infrared spectra. Computers Computing Technology Agriculture, IV, 124–133.

      [24] Anna Siedliska, Piotr Baranowski, Wojciech Mazurek (2014)., Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data, Computers and Electronics in Agriculture, 106, 66–74. https://doi.org/10.1016/j.compag.2014.05.012.

      [25] Yang Z. R. (2006), Machine learning approaches to bioinformatics. In Science, Engineering, and Biology Informatics, First ed., vol. 3. World Scientific Publishing Co., Pte. Ltd., UK, London.

      [26] Hall, M.A. (1998), Correlation-based feature subset selection for machine learning, PhD thesis, University of Waikato, Hamilton, New Zealand.

      [27] Jasmina Novaković, Perica Strbac, Dusan Bulatović (2011), Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms, Yugoslav Journal of Operations Research, 21(1), 119-135. https://doi.org/10.2298/YJOR1101119N.

      [28] Witten I.H., Frank E. (2005), Data mining, in: Practical machine learning tools and techniques, IInd ed. Morgan Kaufmann Publishers/Elsevier, pp. 525.

      [29] Yang Z.R. (2010), Machine learning approaches to bioinformatics, In Science, Engineering, and Biology Informatics, Ist ed., vol. 3. World Scientific Publishing Co., Pvt. Ltd., UK, London.

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  • How to Cite

    V G, N., S. Kini, A., & S. Kini, A. (2018). An intelligent classification model for peanut’s varieties by color and texture features. International Journal of Engineering & Technology, 7(2.27), 250-254. https://doi.org/10.14419/ijet.v7i2.27.12473

    Received date: 2018-05-04

    Accepted date: 2018-06-13

    Published date: 2018-08-23