Bitki Sınıflandırması için Transfer Learning Kullanılarak Topluluk Öğrenmesi Metodu Üzerine Bir Çalışma

Abstract

Derin öğrenme, insana özgü problemlerin gelişmiş donanım gücüne sahip makineler yardımıyla çözüldüğü önemli bir disiplindir. Bu disiplinin sanayi, sağlık, savunma sanayi ve spor alanlarında yaygın olarak kullanıldığı görülmektedir. Ayrıca bahçecilik alanında derin öğrenmenin kullanılması önemli bir gerekliliktir. Derin öğrenmenin bahçeciliğe entegrasyonu ile ürün sınıflandırması yapmak, verimliliği ve üretimi artırmak için oldukça önemlidir. Bu çalışmada çeşitli bitki verilerini kullanarak sınıflandırma probleminin doğruluğunu artırmak için topluluk öğrenmesi yöntemi önerilmiştir. Bu yöntem için veri artırmadan bağımsız olarak toplam 24421 görüntü ve 15 ürün sınıfı içeren yeni bir veri seti oluşturulmuştur. Önerilen yöntem yardımıyla oluşturulan bu veri setini eğitmek için bir modelin çıktısının diğer modelin girdisi olduğu hiyerarşik bir yapı tasarlanmıştır. Önerilen yöntemin deneysel çalışmalarında toplam 7 adet önceden eğitilmiş model kullanılmıştır. Bu yöntem bir topluluk yapısında olduğu için yapıya önceden eğitilmiş modeller eklemek veya çıkarmak mümkündür. Deneysel çalışmalar yardımıyla önerilen yöntemin geleneksel CNN yöntemi ile karşılaştırılan performans analizi yapılmıştır. Bu analizler sonucunda önerilen yöntemin %3 daha başarılı çalıştığı görülmüştür.

Keywords

• Transfer learning
• ensemble learning
• convolutional neural network
• image classification
• deep learning.

A Study of Ensemble Deep Learning Method Using Transfer Learning for Horticultural Data Classification

Abstract

Deep learning is an important discipline in which human-specific problems are solved with the help of machines with advanced hardware power. It is seen this discipline is widely used in the fields of industry, health, defense industry, and sports. In addition, the use of deep learning in the field of horticulture is an important requirement. With the integration of deep learning into horticulture, to do product classification is very important for increasing productivity and production. In this study, a method using ensemble learning is proposed to improve the accuracy of the classification problem for horticultural data. For this method, a new dataset was created, containing a total of 24421 images and 15 crop classes, independent of data augmentation. In order to train this created data set with the help of the proposed method, a hierarchical structure has been designed in which the output of one model is the input of the other model. A total of 7 pre-trained models were used in the experimental studies of the proposed method. Since this method is in an ensemble structure, it is possible to add or remove pre-trained models from the structure. With the help of experimental studies, a performance analysis of the proposed method, which is compared with the traditional CNN method, has been made. As a result of these analyses, it has been observed that the proposed method works 3% more successfully.

Keywords

• Transfer learning
• ensemble learning
• convolutional neural network
• image classification
• deep learning.

Thanks

We would like to thank the Sakarya University of Applied Science Robot Technologies and Intelligent Systems Application and Research Center (ROTASAM) for providing all kinds of opportunities for the realization of this study.

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