Baklagil Islahında Makine Öğrenimi: Genotip ve Çevre Etkileşimlerinin Modellenmesi
DOI:
https://doi.org/10.5281/zenodo.14253789Anahtar Kelimeler:
makine öğrenmesi, baklagiller, bitki ıslahı, modelleme, yapay sinir ağları, genotipx çevre interaksiyonuÖzet
Bu derlemede, baklagil ıslahında genotip-çevre (G×E) etkileşimlerinin modellenmesinde makine öğrenimi tekniklerinin önemi ele alınmaktadır. Tarımsal üretim, iklim değişikliği ve çevresel stres faktörlerinden büyük ölçüde etkilenmektedir ve bu etkileşimlerin daha iyi anlaşılması, çevresel olarak uyumlu ve yüksek verimli çeşitlerin geliştirilmesi için kritik öneme sahiptir. Genotip-çevre etkileşimleri, genetik ve çevresel faktörlerin bitki performansını nasıl etkilediğini anlamak için kritik öneme sahiptir. Çalışmada, destek vektör makineleri (SVM), rastgele ormanlar (RF), derin öğrenme (DL) ve sinir ağları (ANN) gibi makine öğrenimi algoritmalarının yüksek boyutlu veri setlerini işlemek, genotip stabilitesini değerlendirmek ve çevresel streslere tepkiyi modellemek için kullanıldığı bildirilmiştir. Bu teknikler baklagillerde verim, kalite ve adaptasyon gibi karmaşık özelliklerin değerlendirilmesinde önemli avantajlar sunmaktadır. Makine öğrenimi yaklaşımları, geleneksel yöntemlerin eksikliklerinin üstesinden gelmekte ve özellikle büyük veri setlerinin işlenmesinde, çevresel değişkenlerin modellenmesinde ve genetik performansın tahmin edilmesinde daha güçlü araçlar sağlamaktadır. Bu yöntemlerin etkin kullanımı, tarımsal üretimde sürdürülebilirliği artırmak ve iklim değişikliğine dayanıklı çeşitler geliştirmek için büyük bir potansiyele sahiptir.
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