Meta-Learning-Based Prediction of Different Corn Cultivars from Color Feature Extraction
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
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- Anonymous (2019). Colors RGB. Retrieved in January, 14, 2019 from https://www.w3schools.com/colors/colors_rgb.asp
- 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
- Dechao S T Z (1996). An investigation on morphological discrimination of corn kernels based on neural network. Transactions of The Chinese Society of Agricultural Engineering 1
- Dietterich T (1995). Overfitting and undercomputing in machine learning. ACM Computing Surveys 27(3): 326-327
- 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 14(2): 119-128
- Fakı B M (2015). Classification of anemia using data mining methods: an application. MSc. Thesis, Istanbul Technical University, Graduate School of Natural and Applied Sciences, Department of Industrial Engineering, Engineering Management Division, TURKEY
- FAO (2019a). Production quantities of maize by country as the average of 1994-2017 years. Retrieved in January, 11, 2019 from http://www.fao.org/faostat/en/?#data/QC/visualize
- FAO (2019b). Production share of maize by region as the average of 1994-2017 years. Retrieved in January, 11, 2019 from http://www.fao.org/faostat/en/?#data/QC/visualize
- 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 5 (5): 6261-6264
- Jiang J, Wang Y, Yang R & Mei S (2012). Variety identification of corn seed based on bregman split method. Transactions of the Chinese Society of Agricultural Engineering 28(1): 248-252
- 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, ISBN: 6059594349, Istanbul, Turkey
- Kurtulmus F & Ünal H (2015). Discriminating rapeseed varieties using computer vision and machine learning. Expert Systems with Applications 42(2015):1880-1891
- Küçükönder H, Vursavuş K K & Üçkardeş F (2015). Determining the effect of some mechanical properties on color maturity of tomato with k-star, random forest and decision tree (C4. 5) classification algorithms. Turkish Journal of Agriculture-Food Science and Technology 3(5): 300-306
- Li P, Wu Q & Burges C J (2008). Mcrank: Learning to rank using multiple classification and gradient boosting. In Advances in Neural Information Processing Systems, pp. 897-904
- 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
- Mitchell T M (1997). Machine learning. McGraw-Hill Inc., New York, USA
- Ni B, Paulsen M R & Reid J F (1997). Corn kernel crown shape identification using image processing. Transactions of the ASAE 40(3): 833- 838
- Öztemel E (2012). Artificial Neural Networks. 3rd Edition, Papatya Publishing, ISBN: 975-6797-39-8, 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 117(16): 18-21
- Piotrowski A P & Napiorkowski J J (2013). A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. Journal of Hydrology 476: 97-111
- 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, AAAI/MIT Press.
- Shin J, Chang Y K, Nguyen-Quang T, Heung B & Ravichandran P (2019). Optimizing parameters for image processing techniques using machine learning to detect powdery mildew in strawberry leaves. American Society of Agricultural and Biological Engineers, In 2019 ASABE Annual International Meeting, pp. 1
- 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 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70
- Silahtaroğlu G (2016). Data Mining - Concepts and Algorithms. 3. Printing, Papatya Publishing, ISBN: 978-975-6797-81-5, Istanbul, Turkey
- Şeker Ş E & Erdoğan D (2018). End-to-end data science with KNIME. 1. Edition, Demet Erdoğan Publishing House
- Stanı̇ savljevı̇ ć R, Kneževı̇ ć J, Tomı̇ ć D, Jovanovı̇ ć D, Tmušı̇ ć N, Štrbanovı̇ ć R, Poštı̇ ć D (2019). Seed Quality of Oilseed Rape Varieties with Different Size and Colors After Three and Fifteen Months Storage. Journal of Agricultural Sciences 25(4): 449-458 DOI: 10.15832/ankutbd.442650
- 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 25(2): 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). Retrieved in January, 10, 2019 from http://www.tuik.gov.tr/UstMenu.do?metod=temelist
- Yang X, Hong H, You Z, Cheng F (2015). Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification. Sensors 2015(15): 15578-15594
- Zhang J X, Xun Y & Li W (2007). Detection of surface cracks of corn kernel based on morphology. Optics and Precision Engineering 15(6): 951-956
- Zhao M, Wu W, Zhang Y Q & Li X (2011). Combining genetic algorithm and SVM for corn variety identification. 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 19-22August, Jilin, China, pp. 990-993
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