Yağ Gülü (Rosa damascena Mill.) Bitkisinin Hasat Durumunun Makine Öğrenmesi ve Derin Öğrenme Yöntemleri ile Tespiti

Year 2022, , 1328 - 1341, 31.12.2022

Burhan Duman , Kıyas Kayaalp

https://doi.org/10.31202/ecjse.1134822

Abstract

Bitkiler uzun yıllardır çoğu sektörde insan hayatında önemli bir yer tutmaktadır. Pembe Yağ Gülü olarak adlandırılan Rosa damascena Mill. bitkisi, gül çeşitleri arasında kendine özgü keskin ve yoğun kokusu ile kozmetik, parfüm, ilaç ve gıda endüstrisi gibi sektörler için ekonomik değeri olan bir türdür. Türkiye’de Mayıs aylarında hasadına başlanan yağ gülü, tomurcuklarının çiçek açması durumunda hasadı yapılmaktadır. Tomurcuk halindeki güller ise açması durumuna kadar hasat edilmeden bırakılmaktadır. Bu çalışmada makine öğrenmesi ve derin öğrenme yöntemleri kullanarak her bir yağ gülünün “hasat edilebilir/hasat edilemez” durumuna göre ikili sınıflandırılması gerçekleştirilmiştir. Gül bahçelarinden elde edilen görüntüler ile oluşturulan veri seti, yapay zekâ modellerinin eğitim ve testinde kullanılmıştır. Makine öğrenmesi modeli olarak DVM sınıflandırıcısı, derin öğrenme modelleri olarak da VGG16, VGG19 ve InceptionV3 kullanılmıştır. Sınıflandırma başarımı; DVM modelinde %71.06, VGG16 modelinde %96.44, VGG19 modelinde %97.96 ve InceptionV3 modelinde %72.08 olarak elde edilmiştir.

Keywords

Makine öğrenmesi, Derin öğrenme, Rosa damascena Mill., DVM, VGG16, VGG19, InceptionV3

Detection of Harvest Status of Oil Rose (Rosa damascena Mill.) with Machine Learning and Deep Learning Methods

Abstract

Plants have an important place in human life in many sectors for many years. Rosa damascena Mill plant, which is called Pink Oil Rose, is a species that has economic value for sectors such as cosmetics, perfume, medicine and food industry with its distinctive sharp and intense scent among rose varieties. Oil rose is harvested in May in Turkey when its buds bloom. Roses in bud form are left unharvested until they bloom. In this study, binary classification of each oil rose according to "harvestable/non-harvestable" status was carried out using machine learning and deep learning methods. The data set created with the images obtained from the rose gardens was used in the training and testing of artificial intelligence models. DVM classifier was used as machine learning model, and VGG16, VGG19 and InceptionV3 were used as deep learning models. Classification performance is 71.06% in the DVM model, 96.44% in the VGG16 model, 97.96% in the VGG19 model and 72.08% in the InceptionV3 model.

Keywords

Machine learning, Deep learning, Rosa damascena Mill., InceptionV3, VGG16, VGG19

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