Adem TUNCER, Yunus OZEN, Melike SARDOGAN

Faster R-CNN Kullanarak Elma Yaprağı Hastalıklarının Tespiti

Görüntü tanıma tabanlı otomatik hastalık algılama sistemleri, bitkilerde görülen yaprak hastalıklarının erken tespitinde önemli bir rol oynamaktadır. Bu çalışmada, Inception v2 mimarisi ile Daha Hızlı Bölgesel Evrişimsel Sinir Ağı (Faster R-CNN) kullanılarak bir elma yaprağı hastalığı tespit sistemi önerilmiştir. Hastalıkların tespiti için uygulamalar Türkiye’nin Yalova ilindeki elma bahçelerinde gerçekleştirilmiştir. Yaprak görüntüleri iki yıl boyunca farklı elma bahçelerinden elde edilmiştir. Yaptığımız gözlemlerde Yalova'nın elma ağaçlarında özellikle kara leke hastalığının olduğu tespit edilmiştir. Çalışmada önerilen sistem bir görüntü içerisindeki çok fazla sayıda bulunan yaprakları tespit etmekte, ardından hastalıklı ve sağlıklı olanları başarılı bir şekilde sınıflandırmaktadır. Eğitilen hastalık tespit sistemi ortalama %84.5 doğruluk elde etmiştir.

Anahtar Kelimeler:

Evrişimsel sinir ağı (ESA), Daha hızlı bölgesel-ESA, Yaprak hastalığı tespiti

Detection of Apple Leaf Diseases using Faster R-CNN

Image recognition-based automated disease detection systems play an important role in the early detection of plant leaf diseases. In this study, an apple leaf disease detection system was proposed using Faster Region-Based Convolutional Neural Network (Faster R-CNN) with Inception v2 architecture. Applications for the detection of diseases were carried out in apple orchards in Yalova, Turkey. Leaf images were obtained from different apple orchards for two years. In our observations, it was determined that apple trees of Yalova had black spot (venturia inaequalis) disease. The proposed system in the study detects a large number of leaves in an image, then successfully classifies diseased and healthy ones. The disease detection system trained has achieved an average of 84.5% accuracy.

Keywords:

Convolutional neural network (CNN), Faster R-CNN, Leaf disease detection,

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