Classification of Disease in Rice Plant Leaves Using the Method Convolutional Neural Networks

Laila Badriyatuz Zahro; Dafik; Ika Hesti Agustin; Zainur Rasyid Ridlo

doi:10.2991/978-94-6463-174-6_16

<Previous Article In Volume

Next Article In Volume>

Classification of Disease in Rice Plant Leaves Using the Method Convolutional Neural Networks

Authors

Laila Badriyatuz Zahro¹, Dafik²^{, 3}, Ika Hesti Agustin¹^{, *}, Zainur Rasyid Ridlo²^{, 4}

¹Department of Mathematics, University of Jember, Jember, Indonesia

²PUI-PT Combinatroics and Graph, CGANT, University of Jember, Jember, Indonesia

³Department of Mathematics Education Postgraduate, University of Jember, Jember, Indonesia

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

^*Corresponding author. Email: ikahesti.fmipa@unej.ac.id

Corresponding Author

Ika Hesti Agustin

Available Online 22 May 2023.

DOI: 10.2991/978-94-6463-174-6_16 How to use a DOI?
Keywords: rice plant diseases; convolutional neural networks; image classification
Abstract: Rice plant disease is one of the factors causing high losses due to crop failure. Plant-disturbing organisms often attack rice plants, especially on the leaves. This can damage rice plants and cause crop failure. Manual diagnostic activities on rice plant leaves will help identify and classify the types of diseases suffered by rice plant leaves. This study aims to be able to detect diseases that occur in the leaves of rice plants using the Convolutional Neural Network (CNN) method. Convolutional Neural Network is one method that is quite effective for image classification. Image will go through the Pre-Processing, Feature Extraction and Evaluation processes. The dataset used is RiceLeafs Diseases from kaggle with a total of 3000 samples of rice leaf images, 2100 images for training and 300 images for testing. In our research we used 3 different epoch numbers to find the value that produces the highest accuracy. Based on the research, it was found that 75 epochs had the highest accuracy value namely 85.67%.
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.

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_16
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-174-6_16 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.

Cite this article

ris enw bib

TY  - CONF
AU  - Laila Badriyatuz Zahro
AU  - Dafik
AU  - Ika Hesti Agustin
AU  - Zainur Rasyid Ridlo
PY  - 2023
DA  - 2023/05/22
TI  - Classification of Disease in Rice Plant Leaves Using the Method Convolutional Neural Networks
BT  - Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022)
PB  - Atlantis Press
SP  - 195
EP  - 216
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-174-6_16
DO  - 10.2991/978-94-6463-174-6_16
ID  - Zahro2023
ER  -

download .riscopy to clipboard