Prediction of Egg Weight Using MARS data mining Algorithm through R

Internal and external quality characters of poultry eggs are quitelyimportant to determine egg weight. Also, the quality of eggs isimportant for both hatching and egg production. The purpose of thisstudy was modelling of egg weight with the MARS (MultivariateAdaptive Regression Splines) method using inner and outsider qualitycharacters of egg in Lohmann LSL Classic white hybrit flock. For thispurpose, the eggs of the Lohmann LSL Classic white hybrid flock(n=60) were used. Weekly egg yields were evaluated from the 22ndweek to the 62nd week. In the research, for the prediction ofdependent and continuous variable egg weight; shape index (SI), shellbreaking 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 wasdetermined such as EW = 63.1-0.906 * max (0,75-SI)-0.32 * max (0, SI75) -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 eggweight. Variables such as group a2, SI, YC, ST, YD, YH to estimatethe weight of the egg determined as the dependent variable were used.Other variables are not included in this equation. In the poultry, theMARS prediction model may be a better alternative to classicalnonlinear models in predicting egg weight since that it is easier andhas higher accuracy.

R kullanarak Mars Veri Madenciliği Algoritması ile Yumurta Ağırlığı Tahmini

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)*YD0.02*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.

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KSÜ Tarım ve Doğa Dergisi-Cover
  • ISSN: 2619-9149
  • Yayın Aralığı: Yılda 6 Sayı
  • Başlangıç: 2018
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