The Comparison of Convolutional Neural Networks Architectures on Classification Potato Leaf Diseases

Rifki Ilham Baihaki; Dafik; Ika Hesti Agustin; Zainur Rasyid Ridlo; Elsa Yuli Kurniawati

doi:10.2991/978-94-6463-174-6_12

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The Comparison of Convolutional Neural Networks Architectures on Classification Potato Leaf Diseases

Authors

Rifki Ilham Baihaki¹, Dafik¹^{, 2}, Ika Hesti Agustin¹^{, 3}, Zainur Rasyid Ridlo¹^{, 4}^{, *}, Elsa Yuli Kurniawati¹

¹PUI-PT Combinatorics and Graph, CGANT, University of Jember, Jember, Indonesia

²Departement of Mathematics Education Postgraduate, University of Jember, Jember, Indonesia

³Departement of Mathematics, University of Jember, Jember, Indonesia

⁴Department of Science Education, University of Jember, Jember, Indonesia

^*Corresponding author. Email: zainur.fkip@unej.ac.id

Corresponding Author

Zainur Rasyid Ridlo

Available Online 22 May 2023.

DOI: 10.2991/978-94-6463-174-6_12 How to use a DOI?
Keywords: smart agricultural; leaf disease image classification; deep neural networks
Abstract: Potato is a plant from the Solanaceae tribe and one of the staple crops for human consumption. Potatoes have several benefits such as being low in fat and having a better carbohydrate content than rice. Behind the relatively easy cultivation of potato plants, there are problems that are often faced by farmers. This problem is the susceptibility of potato plants to disease. An emerging solution is to combine computer vision and deep learning. This research compared four deep learning architectures such as Alexnet, GoogleNet, ResNet-50, and VGG-16. The best model was produced by VGG-16 with a test accuracy of 99.67%.
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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Volume Title: Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
Series: Advances in Intelligent Systems Research
Publication Date: 22 May 2023
ISBN: 10.2991/978-94-6463-174-6_12
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-174-6_12 How to use a DOI?
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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ris enw bib

TY  - CONF
AU  - Rifki Ilham Baihaki
AU  - Dafik
AU  - Ika Hesti Agustin
AU  - Zainur Rasyid Ridlo
AU  - Elsa Yuli Kurniawati
PY  - 2023
DA  - 2023/05/22
TI  - The Comparison of Convolutional Neural Networks Architectures on Classification Potato Leaf Diseases
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 125
EP  - 145
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_12
DO  - 10.2991/978-94-6463-174-6_12
ID  - Baihaki2023
ER  -

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