Detection of Apple Leaf Diseases using Faster R-CNN

Year 2020, , 1110 - 1117, 31.01.2020

Melike Sardoğan Yunus Özen , Adem Tuncer

https://doi.org/10.29130/dubited.648387

Cited By: 12

Abstract

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

Supporting Institution

Research Fund of Yalova University

Project Number

2018/AP/0001

Faster R-CNN Kullanarak Elma Yaprağı Hastalıklarının Tespiti

Abstract

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.

Keywords

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

Project Number

2018/AP/0001

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