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

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Details

Primary Language

English

Subjects

Agricultural, Veterinary and Food Sciences

Journal Section

Research Article

Authors

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

Esra Yavuz
0000-0002-5589-297X
Türkiye

Ercan Efe
0000-0002-5131-323X
Türkiye

Publication Date

February 28, 2021

Submission Date

April 9, 2020

Acceptance Date

June 30, 2020

Published in Issue

Year 2021 Volume: 24 Number: 1

DOI

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

IZ

https://izlik.org/JA58EG85ML

Cite

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

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https://doi.org/10.18016/ksutarimdoga.vi.1883524

Prediction of Egg Weight Using MARS data mining Algorithm through R

Prediction of Egg Weight Using MARS data mining Algorithm through R

Abstract

Keywords

R kullanarak Mars Veri Madenciliği Algoritması ile Yumurta Ağırlığı Tahmini

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Cited By

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

Bibliometric Analysis for Use of Time Series in Animal Science

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Türkiye’de Yabancılara Konut Satışının Mars Yöntemi ile Tahmin Edilmesi

Prediction of internal egg quality traits of Potchefstroom Koekoek and Lohmann Brown layers using classification and regression tree method

The Influences of Maternal Hormones on The Quality Characteristics of Incubating Eggs