Shearlet dönüşüm ve yeni geometrik özellikler kullanılarak aşırı öğrenme makinesine dayalı bitki tanıma sistemi

Bitki türlerini doğru tespit edebilmek için bugüne kadar yapılan çalışmalarda farklı yaklaşımlar kullanılmıştır. Bu yaklaşımlardan en temel olan bitki yaprakları, şekil, renk ve damar dokusu gibi avantajlara sahip birçok özellik içermektedir. Bu çalışmada açıdan bağımsız olarak yaprağın geometrik özelliklerine dayalı yeni bir yaklaşım önerilmiştir. Kenar Adım (KA) olarak adlandırılan bu yöntem, şeklin sınır eğrilerindeki kenar noktalar kullanılarak açı, merkez-kenar uzunluğu ve kenar mesafesi gibi özelliklerden oluşmaktadır. Ayrıca doku tanımada iyi hassasiyet göstermesi, hızlı hesaplama yapması ve yön bağımsızlığı gibi özelliklere sahip olan Shearlet Dönüşüm yöntemi kullanılmıştır. Bu yöntemlere ek olarak renk özellikleri ile Gri Seviye Eş-Oluşum Matrisleri (GSEM) yöntemi uygulanmıştır. Tüm bu yöntemlerden elde edilen öznitelikler ayrı ayrı ve bileşik olarak Aşırı Öğrenme Makineleri (AÖM) sınıflandırıcı yöntemi ile test edilmiştir. Flavia, Swedish, ICL ve Foliage gibi dört farklı bitki yaprak veri setleri kullanılarak önerilen çalışma test edilmiştir. Bu veri setleri kullanılarak doku, şekil ve renk özelliklerine dayalı yapılan çalışmalar ile önerilen yaklaşımın performansı kıyaslanmıştır. Sonuç olarak, önerilen çalışmanın diğer çalışmalara göre daha başarılı olduğu tespit edilmiştir.

Anahtar Kelimeler:

Yaprak Tanıma, Görüntü İşleme, Shearlet Dönüşüm, Kenar Adım Yöntemi, Aşırı Öğrenme Makinesi

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