@article{article_648387, title={Detection of Apple Leaf Diseases using Faster R-CNN}, journal={Duzce University Journal of Science and Technology}, volume={8}, pages={1110–1117}, year={2020}, DOI={10.29130/dubited.648387}, author={Sardoğan, Melike and Özen, Yunus and Tuncer, Adem}, keywords={Evrişimsel sinir ağı (ESA),Daha hızlı bölgesel-ESA,Yaprak hastalığı tespiti}, abstract={<p class="MsoNormal" style="margin-bottom:.0001pt;text-align:justify;line-height:normal;"> <span lang="en-us" style="font-size:10pt;font-family:’Times New Roman’, serif;" xml:lang="en-us">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. <span style="color:#FF0000;"> </span>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. </span> </p> <p> </p>}, number={1}, publisher={Düzce Üniversitesi}, organization={Research Fund of Yalova University}