Yapay sinir ağları ile makarnalık buğday çeşitlerinin sınıflandırılması

Bu çalışmada, makarnalık buğday çeşitlerinin sınıflandırmasını yapmak amacıyla bir Yapay Sinir Ağları (YSA) modeli geliştirilmiştir. Bu amaçla makarnalık buğdaylara ait fiziksel özellikler belirlenmiş ve YSA teknikleri kullanılmıştır. Ülkemizde yetiştirilmekte olan on bir adet makarnalık buğday çeşidinin fiziksel özelliklerinden olan bin dane ağırlığı, geometrik ortalama çap, küresellik, dane hacmi, yüzey alanı, hacim ağırlığı, özgül ağırlık, porozite ve renk belirlenmiş ve bu özelliklerin çeşitlere göre istatistiksel olarak farklı olduğu tespit edilmiştir. YSA modeli olarak M-l, M-ll ve M-lll olmak üzere üç adet model geliştirilmiştir. Bu modellerin performansları karşılaştırılmıştır. En uygun modelin M-l modeli olduğu belirlenmiştir. M-l modelinde ağın yapısı, 11 giriş, 2 ara ve 1 çıkış katmanı olarak dizayn edilmiştir. Giriş parametresi olarak bin dane ağırlığı, geometrik ortalama çap, küresellik, dane hacmi, yüzey alanı, hacim ağırlığı, özgül ağırlık, porozite ve renk, çıkış parametresi olarak ise çeşitler kullanılmıştır. M-l modeli için R2 değeri %99.99, RMSE değeri 0.00074 ve ortalama hata değeri %0.009 olarak bulunmuştur. M-l modeli ile elde edilen tüm sonuçların gerçek veriler ile uyumluluk içinde olduğu görülmüştür. Bu model ile Ticaret Borsaları ve un fabrikalarında ürünleri sınıflandırma ve temizleme amacıyla otomasyon sistemleri oluşturmak mümkün olabilecektir.

Classification of durum wheat varieties by artificial neural networks

In this study, an Artificial Neural Network (ANN) was developed in order to classify durum wheat varieties. For this purpose, physical properties of durum wheat varieties were determined and ANN techniques were used. The physical properties of 11 durum wheat varieties grown in our country, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and color parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, M-l, M-ll and M-lll were constructed. The performances of these models were compared. It was determined that the best fit model was M-1. In the M-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and color parameters of grain were used as input parameter; and varieties as output parameter. R2, RMSE and mean error for the M-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the M-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in Commodity (Grain) Exchange and flour mills.

Keywords:

Artificial neural networks, durum wheat, physical properties,

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