Combination of Deep Features and KNN Algorithm for Classification of Leaf-Based Plant Species Yaprak Tabanlz Bitki Tiirlerinin Stntflandirtlmast icin Derin Ozellikler ve KNNAlgoritmastnin Kombinasyonu

TÜRKOĞLU M., HANBAY D.

2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019, Malatya, Türkiye, 21 - 22 Eylül 2019, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/idap.2019.8875911
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: AlexNet model, Convolutional Neural Networks, KNN classifier, Plant Classification, VGG16 model
  • Samsun Üniversitesi Adresli: Hayır

Özet

Recently, Convolutional Neural Networks (CNN), which is used in the solution of many image processing problems, has been used successfully for many problems in the agricultural field. In this study, for classification of plant species is proposed an approach based on the combination of deep architectures. Deep features were extracted from the plant leaves using the fc6 layer of the previously trained AlexNet and VGG16 models. Then, the reduction of the number of deep features by using the Principal Component Analysis (PCA) method was done quickly and the best distinguishing features were obtained. Finally, the classification performances were calculated using the K-Nearest Neighbor (KNN) method. Flavia and Swedish plant leaf data sets were used to test the proposed system. According to the experimental results, the accuracy scores for Flavia and Swedish data sets was obtained as 99.42% and 99.64%, respectively.