Meta-learning Based Prediction of Different Corn Cultivars from Colour Feature Extraction with Image Processing Technique
نویسندگان
چکیده
Image analysis techniques are developing as applicable to the approaches of quantitative analysis, which is aimed determine cultivar grains. Additionally, corn (Zea mays) grain processing companies evaluate quality kernels price these cultivars. Because this reason, in study, a computer image technique was applied on three These were Zea mays L. indentata, saccharata and hybrid (Yellow sweet corn). cultivars commercially important dry grains Turkey. In color values tested from Turkey’s collection. One hundred samples used for each cultivar, 300 total evaluations. Each nine parameters (Rmin, Rmean, Rmax, Gmin, Gmean, Gmax, Bmin, Bmean, Bmax) obtained original RGB channels with maximum minimum evaluated digital images different The analyzed help Multilayer Perceptron (MLP), Decision Tree (DT), Gradient Boost (GBDT) Random Forest (RF) algorithms by using Knime Analytics Platform. majority voting method MLP DT prediction fusion. All run 10-fold cross-validation method. success accuracy found 99% RF GBDT, 97.66% MLP, 96.66% 97.40% Majority Voting (MAVL). MAVL increased while decreasing partly fusion DT.
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ژورنال
عنوان ژورنال: Tarim Bilimleri Dergisi-journal of Agricultural Sciences
سال: 2021
ISSN: ['2148-9297', '1300-7580']
DOI: https://doi.org/10.15832/ankutbd.567407