Prediction of Egg Weight Using MARS data mining Algorithm through R

Demet Çanga; Esra Yavuz; Ercan Efe

doi:10.18016/ksutarimdoga.vi.716880

EN TR

Prediction of Egg Weight Using MARS data mining Algorithm through R

Abstract

Internal and external quality characters of poultry eggs are quitely important to determine egg weight. Also, the quality of eggs is important for both hatching and egg production. The purpose of this study was modelling of egg weight with the MARS (Multivariate Adaptive Regression Splines) method using inner and outsider quality characters of egg in Lohmann LSL Classic white hybrit flock. For this purpose, the eggs of the Lohmann LSL Classic white hybrid flock (n=60) were used. Weekly egg yields were evaluated from the 22nd week to the 62nd week. In the research, for the prediction of dependent and continuous variable egg weight; shape index (SI), shell breaking resistance (SBS), shell weight (SW), shell thickness (ST), yolk diameter (YD), yolk width (YW), yolk height (YH), color (YC ), albumen length (AW), albumen height (AL) and albumen height (AH) were used. In order to obtain perfect goodness of fit, in the “earth” package of the R program, the definitions of penalty -1, degree = 2, nprune = 10 and nk = 60. The research, the mars prediction model was determined such as EW = 63.1-0.906 * max (0,75-SI)-0.32 * max (0, SI-75) -62.4 * max (0,0.57-ST) -354 * max (0, ST-0.57) + 1.13 * Groupa2 * max (0, 75-SI) + 1.49 * (0.0.57-ST) max * YD + 8.2*max(0, ST 0.57) * YD-0.02*(0 YD-38.5)max* YC-0.0366*YH * max(0,13-YC). As a result, some quality variables were found to be important in determining egg weight. Variables such as group a2, SI, YC, ST, YD, YH to estimate the weight of the egg determined as the dependent variable were used. Other variables are not included in this equation. In the poultry, the MARS prediction model may be a better alternative to classical nonlinear models in predicting egg weight since that it is easier and has higher accuracy.

Keywords

R kullanarak Mars Veri Madenciliği Algoritması ile Yumurta Ağırlığı Tahmini

Öz

Kanatlı hayvanlarda, yumurta ağırlığını belirlemede yumurtanın iç ve dış kalite özellikleri oldukça önemlidir. Yumurtanın kalite özellikleri, gerek kuluçka üretimi ve gerekse yemeklik yumurta üretimi açısından büyük önem taşımaktadır. Bu çalışmanın amacı, Lohmann LSL Classic beyaz hibrit sürü yumurtaları kullanılarak yumurtanın ic dış kalite özellikleri ile yumurta ağırlığının tahminini MARS (Multivariate Adaptive Regression Splines) yöntemi ile modellemektir. Bu amacı gerçekleştirmek için Lohmann LSL Classic beyaz hibrit sürü (n = 60) yumurtaları kullanıldı. Haftalık yumurta verimleri 22. haftadan 62. haftaya kadar değerlendirilmiştir. Bağımlı ve sürekli değişken olarak belirlenen yumurta ağırlığını (EW) tahmin etmek için; şekil indeksi (SI), kabuk kırılma mukavemeti (SBS), kabuk ağırlığı (SW), kabuk kalınlığı (ST), yumurta sarısı çapı (YD), yumurta sarısı genişliği (YW), yumurta sarısı yüksekliği (YH), yumurta sarısı rengi (YC) albümin genişliği (AW), albümin uzunluğu (AL), albümin yüksekliği (AH) kullanılmıştır. Mükemmel uyum iyiliği elde etmek için, R programının “earth” paketinde, penalty = -1, derece = 2, nprune = 10 ve nk = 60 tanımları yapıldı. Araştırma sonucunda mars tahmin modeli, EW = 63.1-0.906 * max (0,75-SI) -0.321 * max (0, SI-75)-62.4*max(0,0.57-ST)-354*max(0,ST 0.57)+1.13*Groupa2*max (0,75-SI)+1.49* max(0.0.57-ST) * YD + 8.2 * max(0, ST-0.57)*YD-0.02*max(0 YD-38.5)*YC-0.0366* YH*max(0,13-YC) olarak belirlendi. Sonuç olarak, bazı kalite değişkenlerinin yumurta ağırlığının belirlenmesinde önemli olduğu bulunmuştur.Bağımlı değişken olarak belirlenen yumurtanın ağırlığını tahmin ederken a2, SI, YC, ST, YD, YH görülürken, diğer değişkenler bu denkleme dahil edilmemiştir. Tavukçulukta, MARS tahmin modeli, daha kolay formül ve daha yüksek doğruluk ile yumurta ağırlığını tahmin etmede klasik lineer olmayan modellere daha iyi bir alternatif olabilir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Ziraat, Veterinerlik ve Gıda Bilimleri

Bölüm

Araştırma Makalesi

Yazarlar

Demet Çanga ^*
0000-0003-3319-7084
Türkiye

Esra Yavuz
0000-0002-5589-297X
Türkiye

Ercan Efe
0000-0002-5131-323X
Türkiye

Yayımlanma Tarihi

28 Şubat 2021

Gönderilme Tarihi

9 Nisan 2020

Kabul Tarihi

30 Haziran 2020

Yayımlandığı Sayı

Yıl 2021 Cilt: 24 Sayı: 1

DOI

https://doi.org/10.18016/ksutarimdoga.vi.716880

IZ

https://izlik.org/JA58EG85ML

Kaynak Göster

RIS / Bibtex

APA

Çanga, D., Yavuz, E., & Efe, E. (2021). Prediction of Egg Weight Using MARS data mining Algorithm through R. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi, 24(1), 242-251. https://doi.org/10.18016/ksutarimdoga.vi.716880