Deep Learning Architectures Performance in Plant Leaf Diseases


Abstract views: 16 / PDF downloads: 61

Authors

  • Yunus Emre Karaca Department of Informatics / Graduate School of Education, Malatya Turgut Ozal University, Türkiye
  • Serpil Aslan Department of Software Engineering / Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Türkiye
  • Muhammed Yıldırım Department of Software Engineering / Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, Türkiye

DOI:

https://doi.org/10.59287/iccar.776

Keywords:

Artificial Neural Network, Deep Learning, Image Processing, Artificial Intelligence, Crop, Agriculture, Sugar Can

Abstract

Developing artificial intelligence applications continue to make our lives easier. Image processing technology has been developed for field-useful studies in medical science, education, finance, agriculture, industry, security, and many other sectors. For agricultural products, good works are done with artificial intelligence to detect plant diseases and take precautions accordingly. In our study, a comparison was made with deep learning methods on the images of the sugar cane plant in different categories. The VGG-19 architecture, which was classified separately from the 5 pre-trained architectures AlexNet, DarkNet-53, GoogLeNet, ResNet-50, and VGG-19, reached the highest accuracy with 92.2%.

Downloads

Published

2023-05-24

How to Cite

Karaca, Y. E., Aslan, S., & Yıldırım, M. (2023). Deep Learning Architectures Performance in Plant Leaf Diseases. International Conference on Contemporary Academic Research, 1, 180–186. https://doi.org/10.59287/iccar.776