LeNet ve ResNet Derin Öğrenme Modelleri ile Asma Yapraklarının Sınıflandırması

Year 2024, Volume: 7 Issue: 1, 16 - 25, 30.06.2024

Kıyas Kayaalp , Aygün Varol

Abstract

Bu çalışmanın temel amacı, asma yapraklarının türlerine göre doğru bir şekilde sınıflandırılmasında derin öğrenme tekniklerinin etkinliğini araştırmaktır. LeNet ve ResNet mimarilerinin entegrasyonu, bu sınıflandırmayı gerçekleştirmenin bir yolu olarak kullanılmıştır. Gerekli veri seti için, beş farklı türü temsil eden 500 asma yaprağı görüntüsünden oluşan kapsamlı bir koleksiyon kullanılmıştır. Sınıflandırma performansını optimize etmek için kritik bir bileşen olarak özellik seçimine önemli bir vurgu yapıldı. İlgili özelliklerin dikkatli bir şekilde seçilmesi ve gereksiz olanların ortadan kaldırılmasıyla, kullanılan modellerin doğruluğunun artırılması amaçlanmıştır. Seçilen derin özelliklerle birlikte LeNet-5 yaklaşımından yararlanılarak %93.99 gibi iyi bir doğruluk oranına ulaşılmıştır. Bu, asma yaprağı sınıflandırması için kullanılan diğer son teknoloji yöntemlerin performansını aşmıştır. Bu kayda değer bulgulara dayanarak, gelecekteki araştırmalar için umut verici birkaç yol belirlenmiştir. Bunlar arasında alternatif derin öğrenme mimarilerinin araştırılması, çeşitli özellik seçim yöntemlerinin kapsamlı bir şekilde incelenmesi ve bu tekniklerin diğer bitki türlerinden yaprakların tanımlanmasını kapsayacak şekilde genişletilmesi yer almaktadır.

Keywords

LeNet-5, Derin öğrenme, Evrişimsel sinir ağları, Görüntü İşleme

Classification of Grapevine Leaves with LeNet and ResNet Deep Learning Models

Abstract

The main objective of this study is to investigate the effectiveness of deep learning techniques in accurately classifying grapevine leaves according to their species. The integration of LeNet and ResNet architectures has been used as a way to realise this classification. For the required dataset, a comprehensive collection of 500 grapevine leaf images representing five different species was used. A significant emphasis was placed on feature selection as a critical component to optimise classification performance. By careful selection of relevant features and elimination of redundant ones, it was aimed to improve the accuracy of the models used. By utilizing the LeNet-5 approach with the selected deep features, a good accuracy of 93.99% was achieved. This exceeded the performance of other state-of-the-art methods used for grapevine leaf classification. Based on these remarkable findings, several promising avenues for future research have been identified. These include the exploration of alternative deep learning architectures, a thorough investigation of various feature selection methods, and the extension of these techniques to cover the identification of leaves from other plant species.

Keywords

LeNet-5, ResNet, Deep Learning, Convolutional Neural Networks, Image Processing

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