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

Abdullah BEYAZ; Dilara GERDAN

doi:https://doi.org/10.15832/ankutbd.567407

Makale detayları

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

Yazarlar:

Abdullah BEYAZ | Dilara GERDAN

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Agricultural Sciences 2021 Vol.27 Issue 1, pp.32-41

Belge Tipi:

Makale

Yayın Tarihi:

3/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000606956

Sıklık:

Üç Aylık

eISSN:

2148-9297

DOI:

https://doi.org/10.15832/ankutbd.567407

Özet:

 

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.

Makale detayları

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

Yazarlar: Abdullah BEYAZ | Dilara GERDAN

Yayıncı: Ankara University

Ana Başlıklar: Uygulamalı fizik

Dergi: Journal of Agricultural Sciences 2021 Vol.27 Issue 1, pp.32-41

Belge Tipi: Makale

Yayın Tarihi: 3/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000606956

Sıklık: Üç Aylık

eISSN: 2148-9297

DOI: https://doi.org/10.15832/ankutbd.567407

Sayı

Yazarlar:

Abdullah BEYAZ | Dilara GERDAN

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Agricultural Sciences 2021 Vol.27 Issue 1, pp.32-41

Belge Tipi:

Makale

Yayın Tarihi:

3/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000606956

Sıklık:

Üç Aylık

eISSN:

2148-9297

DOI:

https://doi.org/10.15832/ankutbd.567407