Abdullah BEYAZ, Dilara GERDAN

Meta-Learning-Based Prediction of Different Corn Cultivars from Color Feature Extraction

Image analysis techniques are developing as applicable to the approaches of quantitative analysis, which is aimed to determine cultivar grains. Additionally, corn (Zea mays) grain processing companies evaluate the quality of kernels to determine the price of these cultivars. Because of this reason, in the study, a computer image analysis technique was applied on three corn cultivars. These were Zea mays L. indentata, Zea mays L. saccharata and a hybrid corn (Yellow sweet corn). These cultivars are commercially important as dry grains in Turkey. In the study, the grain color values were tested in the cultivars from Turkey’s collection. One hundred samples were used for each corn cultivar, and 300 corn grains in total were used for evaluations. Each of nine color parameters (Rmin, Rmean, Rmax, Gmin, Gmean, Gmax, Bmin, Bmean, Bmax) which were obtained from original RGB color channels with maximum and minimum values was evaluated from the digital images of three different corn cultivar grains. The values were analyzed with the help of the Multilayer Perceptron (MLP), Decision Tree (DT), Gradient Boost Decision Tree (GBDT) and Random Forest (RF) algorithms by using the Knime Analytics Platform. The majority voting method was applied to MLP and DT for prediction fusion. All algorithms were run with a 10-fold cross-validation method. The success of prediction accuracy was found as 99% for RF and GBDT, 97.66% for MLP, 96.66% DT and 97.40% for Majority Voting (MAVL). The MAVL method increased the accuracy of DT while decreasing the accuracy of MLP partly for the fusion of MLP and DT.

Keywords:

Corn, Cultivar identification, Image analysis Data mining, Meta-learning,

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Anonymous (2019) Colors RGB. Webpage: https://www.w3schools.com/colors/colors_rgb.asp, / Accessed: 14 January 2019.
Chen X, Xunb Y, Li W, Zhanga J (2010). Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture 71S (2010), 48–53.
Draganova T, Daskalov P, Tsonev R (2010). An Approach for Identifying of Fusarium Infected Maize Grains by Spectral Analysis in the Visible and Near Infrared Region, SIMCA Models, Parametric and Neural Classifiers. International Journal Bioautomation, 2010, 14(2), 119-128.
FAO (2019a). Production quantities of Maize by country as the average of 1994 – 2017 years (2018a). Webpage: http://www.fao.org/faostat/en/?#data/QC/visualize/, Accessed: 11 January 2019.
FAO (2019b). Production share of Maize by region as the average of 1994 – 2017 years. (2018b) Webpage: http://www.fao.org/faostat/en/?#data/QC/visualize /, Accessed: 11 January 2019.
Gupte A, Joshi S, Gadgul P, Kadam A (2014). Comparative Study of Classification Algorithms used in Sentiment Analysis. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (5), 2014, 6261-6264.
Kim T, Lee D, Choil J, Spurlock A, Sim A, Todd A, Wu K (2015). Extracting Baseline Electricity Usage with Gradient Tree Boosting. 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China.
Köse İ. (2018). Data Mining Theory Practice and Philosophy. Papatya Education Publishing, (280 pages). 2018, Istanbul, Turkey.
Kurtulmus F & Ünal H (2015). Discriminating rapeseed varieties using computer vision and machine learning. Expert Systems with Applications, 42 (2015), 1880–1891.
Mitchell M W (2011). Bias of the Random Forest Out-of-Bag (OOB) Error for Certain Input Parameters. Open Journal of Statistics, 2011 (1), 205-211 doi:10.4236/ojs.2011.13024
Mitchell T M (1997). Machine Learning. McGraw-Hill, Inc., (432 pages). 1997, New York, USA
Öztemel E. (2012). Artificial Neural Networks. 3rd Edition, Papatya Publishing, 2012, Istanbul, Turkey.
Pandya R & Pandya J (2015). C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning International Journal of Computer Applications 2015, (0975 – 8887) 117(16), 18 – 21.
Prodromidis A, Chan P, Stolfo S (2000). Meta-learning in distributed data mining systems: Issues and approaches, In Advances in Distributed and Parallel Knowledge Discovery, H. Kargupta and P. Chan (editors), 2000, AAAI/MIT Press.
Si S, Zhang H, Keerthi S S, Mahajan D, Dhillon I S, Hsieh C-J (2017). Gradient Boosted Decision Trees for High Dimensional Sparse Output. Proceedings of the 34 th. International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017.
Silahtaroğlu G. (2016). Data Mining Concepts and Algorithms. 3. Printing (304 pages), Papatya Publishing, 2016. Istanbul, Turkey.
Şeker Ş E & Erdogan D. (2018). End-to-End Data Science with KNIME. 1. Edition, 440 p., Demet Erdoğan Publishing House, 2018, ISBN: 9781386738657.
Takran T, Chartrungruang B, Tantranont N, Somhom S (2017). Constructing a Thai Homestay Standard Assessment Model by Implementing a Decision Tree Technique. International Journal of the Computer, the Internet and Management Vol.25 No.2 (May-August, 2017) 106-112.
Tan´ska M, Rotkiewicz D, Kozirok W, Konopka I (2005). Measurement of the geometrical features and surface color of rapeseeds using digital image analysis. Food Research International 38 (2005) 741–750.
Turkish Statistics Institute (TSI) (2018). Area and production of cereals and other crop products (For selected products). http://www.tuik.gov.tr/UstMenu.do?metod=temelist/ accessed 10 January 2019.