Bitki Doku Kültüründe Yapay Zekâ ve Makine Öğrenmesi Uygulamaları
Özet
Bu bölüm, bitki doku kültürü alanında yapay zekâ (YZ) ve makine öğrenmesi (ML) tekniklerinin giderek artan önemini bütüncül ve disiplinler arası bir bakış açısıyla ele almaktadır. Klasik in vitro yaklaşımların çok değişkenli ve doğrusal olmayan biyolojik sistemleri açıklamada karşılaştığı sınırlılıklar, bu bölümde veri temelli ve öngörücü yapay zekâ modelleri çerçevesinde tartışılmaktadır. Yapay zekânın bitki doku kültürüne entegrasyonu; mikroçoğaltım veriminin artırılması, besin ortamı ve hormon kombinasyonlarının optimizasyonu, stres fizyolojisi analizleri, sekonder metabolit üretim tahminleri ve görüntü tabanlı morfolojik değerlendirmeler gibi temel uygulama alanları üzerinden ayrıntılı olarak sunulmaktadır. Bölümde, Yapay Sinir Ağları (ANN), Destek Vektör Makineleri (SVM), Rastgele Orman (RF), K-En Yakın Komşu (KNN) ve Genetik Algoritmalar (GA) gibi yaygın kullanılan yapay zekâ ve optimizasyon yöntemleri; teorik temelleri, güçlü yönleri, sınırlılıkları ve bitki doku kültüründeki özgül kullanım örnekleriyle birlikte karşılaştırmalı biçimde ele alınmaktadır. Ayrıca bu yöntemlerin hibrit ve karar destek sistemleriyle entegrasyonu, protokol geliştirme süreçlerinde deneme-yanılma yaklaşımını nasıl dönüştürdüğü üzerinden değerlendirilmektedir. Bölümün ilerleyen kısımlarında, yapay zekâ destekli biyoreaktör sistemleri ve dijitalleşmiş in vitro üretim altyapıları ele alınarak, bitki biyoteknolojisinde otomasyon, ölçeklenebilirlik ve sürdürülebilirlik perspektifi ortaya konulmaktadır. Bu bölüm, yapay zekâ ve makine öğrenmesini yalnızca yardımcı analiz araçları olarak değil, bitki doku kültüründe yeni bir araştırma ve üretim paradigmasının temel bileşenleri olarak konumlandırmakta; hem akademik araştırmalar hem de ticari uygulamalar için yol gösterici bir çerçeve sunmaktadır.
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