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

Authors : Abdullah BEYAZ, Dilara GERDAN

Pages : 32-41

Doi:10.15832/ankutbd.567407

View : 55 | Download : 13

Publication Date : 2021-03-04

Article Type : Research Paper

Abstract :Image analysis techniques are developing as applicable to the approaches of quantitative analysis, which is aimed to determine cultivar grains. Additionally, corn insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(MLP);, Decision Tree insert ignore into journalissuearticles values(DT);, Gradient Boost Decision Tree insert ignore into journalissuearticles values(GBDT); and Random Forest insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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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