Deep Learning Based Classification of Microscopic Fungi for Agriculture Application

DOI

10.2991/978-94-6463-196-8_42How to use a DOI?

Keywords

Fungi; Leaf diseases; Microscopic images; Machine learning; Deep learning; ResNet-50

Abstract

Plant diseases are one of the significant reasons that lead to the destruction of crops and plants. These diseases are caused by bacteria, virus, algae, fungi, etc. Among these diseases, fungi causes the major diseases in plants and crops. This article aims to collect the novel dataset of fungi infected leaves of two different fruit plants. To take pictures of the fungus at a microscopic scale, these leaves are carefully grown and examined under a microscope with a 40X objective. By utilizing the machine learning classifiers and deep learning architectures We develop and examine the models on the collected novel dataset. Using 5 fold cross validation experimental results showed the high recognition accuracy of 97.52% for the ResNet-50 model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mallikarjun Hangarge
PY  - 2023
DA  - 2023/08/10
TI  - Deep Learning Based Classification of Microscopic Fungi for Agriculture Application
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 546
EP  - 560
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_42
DO  - 10.2991/978-94-6463-196-8_42
ID  - Hangarge2023
ER  -