Farklı Çiçek Türlerini Derin Öğrenme Yöntemi İle Tanıma

Abstract

Bu çalışmada derin öğrenme teknikleri kullanılarak çiçek türlerini tanıyabilen bir algoritma geliştirilmiştir. Bitkileri ve çiçekleri ayırt etmek araştırmacılar, ziraat mühendisleri, orman mühendisleri, çiftçiler ve botanikçiler için önem taşımaktadır. Çiçek türlerini ayırt edebilmek için her çiçeğe özgü özellik ve biçimlerin çıkarılması gerekmektedir. Çiçeklerin karmaşık arka planı, farklı çiçek türleri arasındaki benzerlik ve aynı çiçek türleri arasındaki farklılıklar nedeniyle çiçek görüntülerinin sınıflandırılması zorlu bir görevdir. Bu nedenle çiçek görüntülerinin bilgisayar ortamında tanınması ve gruplandırılması çeşitli kolaylıklar sağlayarak başarımı arttırmaktadır. Bu çalışmada bir çiçeğin görüntüsü sisteme yüklendiğinde çiçeğin türünü tahmin eden bir sistem geliştirilmiştir. Sistemin eğitiminde ve test işlemlerinde Oxford 102 veri seti kullanılmıştır. Bu veri seti 102 farklı çiçek türüne ait 7370 görüntü içermektedir. Çiçek türlerini sınıflandırmak için son yıllarda görüntü işleme konusundaki başarıları nedeniyle ResNet152 derin öğrenme mimarisi kullanılmıştır. Test görüntüleri için %99 sınıflama başarısı gösteren sistemin diğer çiçek sınıflama yöntemlerinden daha başarılı olduğu görülmüştür.

Keywords

• Çiçek sınıflandırma
• derin öğrenme
• ResNet 152
• derin kalıntı ağlar

Recognizing The Different Flower Species By Deep Learning

Abstract

In this study, an algorithm that can recognize flower species using deep learning techniques has been developed. Differentiating plants and flowers is important for researchers, agricultural engineers, forestry engineers, farmers and botanists. In order to distinguish flower types, features and forms specific to each flower need to be extracted. Classifying flower images is a challenging task because of the complex background of flowers, the similarity between different flower types, and the differences between the same flower types. For this reason, the recognition and grouping of flower images in computer environment increases the performance by providing various facilities. In this study, a system has been developed that predicts the type of flower when the image of a flower is loaded into the system. The Oxford 102 data set was used in the training and testing of the system. This data set contains 7370 images of 102 different flower species. Due to its achievements in image processing in recent years, ResNet152 deep learning architecture is used in this study to classify flower species. The system, which showed 99% classification success for test images, was found to be more successful than other flower classification methods.

Keywords

• Flower Classification
• Deep Learning
• ResNet152
• Deep Residual Network

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