A Comparison of Artificial Neural Networks and Some Nonlinear Models of Leaf Area Estimation of Sugar Beet at Different Nitrogen Levels

Year 2018, , 303 - 309, 26.07.2018

Sultan Kıymaz , Ufuk Karadavut Ahmet Ertek

https://doi.org/10.30910/turkjans.448371

Cited By: 1

Abstract

Leaf
area is related to many physiological
and agronomic studies including growth, photosynthesis, transpiration, and
energy balance. The study aimed to
determine the leaf area estimation of
sugar beet (Beta vulgaris L.) at different nitrogen levels under field conditions. The study was conducted out in split plots in
randomized complete blocks with three replications in 2012-2013, and
measurements were taken from leaf parameters, such as length (L) and width (W), petiole length, and the total number of
leaf per a sugar beet. The artificial neural networks and such non-linear
methods as the Logistic, Richards, and Gompertz were compared to estimate the
leaf area measurements. As a result, all models have shown the highest
identification success in the level of third fertilization. While the ANN model in the first three fertilizer doses
showed a higher definition of success compared to other models, the Richards
model in the fourth fertilizer dose has been more successful. An increase in
the nitrogen level has accelerated the plant growth.  While the ANN model remained insufficient for very rapid growth identification, the
Richards model is defined in more successful rapid growth

Keywords

Comparison, non-linear models, neural network model, sugar beet.

Yapay Sinir Ağları ve Bazı Doğrusal Olmayan Modellerin Farklı Azot Seviyelerindeki Şeker Pancarı Yaprak Alan Tahmininin Karşılaştırılması

Abstract

Yaprak alanı, birçok büyüme, fotosentez, terleme ve enerji dengesini
içeren agronomik ve fizyolojik çalışmalarla ilgilidir. Çalışma, tarla
koşullarında farklı azot seviyelerindeki şeker pancarının (Beta vulgaris L.) yaprak alanı tahmininin belirlenmesini
amaçlamıştır. Çalışma, tesadüf bloklarında bölünmüş parseller deneme deseninde
3 tekerrürlü olarak 2012-2013 yıllarında yürütülmüştür. Ölçümler yaprak boyu,
yaprak eni, yaprak sapı uzunluğu ve bitki başına toplam yaprak sayısı gibi
yaprak parametrelerinden alınmıştır. Yapay sinir ağları ve Lojistik, Richards
ve Gompertz gibi doğrusal olmayan yöntemler yaprak alanı ölçümlerini tahmin
etmek için karşılaştırıldı. Sonuç olarak, tüm modeller üçüncü gübreleme
düzeyinde en yüksek tanımlama başarısını göstermiştir. İlk üç gübre dozunda
yapay sinir ağları (YSA) modelinde diğer modellere göre daha yüksek bir başarı
düzeyi gösterilirken, dördüncü gübre dozunda Richards modeli daha başarılı
olmuştur. Azot seviyesinin artması ile bitkinin büyümesi hızlanmaktadır. YSA modeli hızlı büyüme tanımlamasında
yetersiz kalırken, Richards modeli daha hızlı büyümede daha başarılı olarak
tanımlanmıştır.

Keywords

Karşılaştırma, doğrusal olmayan modeller, sinir ağları modeli, şekerpancarı.

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