Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)

<Previous Article In Volume
Next Article In Volume>

Haploid Diploid Maize Seeds Classification Using Residual Network

Authors
Wahyudi Setiawan1, *, Yoga Dwitya Pramudita2
1Department of Information System, University of Trunojoyo Madura, Bangkalan, Indonesia
2Department of Informatics, University of Trunojoyo Madura, Bangkalan, Indonesia
*Corresponding author. Email: wsetiawan@trunojoyo.ac.id
Corresponding Author
Wahyudi Setiawan
Available Online 22 May 2023.
DOI
10.2991/978-94-6463-174-6_6How to use a DOI?
Keywords
Maize Leaves Diseases; Image Classification; Convolutional Neural Network; Residual Network
Abstract

Maize seed breeding is an important basis for getting better production. Maize seeds consist of two types: diploid and haploid. Haploid seed can accelerate maize breeding results in just two to three generations. In contrast to diploid (normal) which requires up to eight generations. In this article, we discuss about the classification of haploid-diploid seeds. The dataset uses rovile public data with a total number of 3,000 images. Training data consists of 2,400 images, the rest is testing data. The data consists of 1,230 haploid and 1,770 diploids. The experiment contained of preprocessing, feature extraction, and classification. Preprocessing using closing morphology. While feature extraction and classification using ResNet50. As a comparison, this study also used VGG16, and MobileNet. The parameters during the training process use epoch 50, batch-size 10, learning rate 0.0001, and Root Means Square Propagation Optimization. The experimental results showed accuracy using ResNet50, VGG16, and MobileNet at 98.16%, 97.83%, and 97.83%, respectively.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
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_6
ISSN
1951-6851
DOI
10.2991/978-94-6463-174-6_6How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Wahyudi Setiawan
AU  - Yoga Dwitya Pramudita
PY  - 2023
DA  - 2023/05/22
TI  - Haploid Diploid Maize Seeds Classification Using Residual Network
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 49
EP  - 59
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_6
DO  - 10.2991/978-94-6463-174-6_6
ID  - Setiawan2023
ER  -