Deep-Clustering Based Plant Disease Segmentation Network

Seong Eui Lee; Sang Ho Lee; Jong Ok Kim

doi:10.1109/ICEIC57457.2023.10049898

Deep-Clustering Based Plant Disease Segmentation Network

Seong Eui Lee, Sang Ho Lee, Jong Ok Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.

Original language	English
Title of host publication	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320213
DOIs	https://doi.org/10.1109/ICEIC57457.2023.10049898
Publication status	Published - 2023
Event	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 - Singapore, Singapore Duration: 2023 Feb 5 → 2023 Feb 8

Publication series

Name	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

Conference

Conference	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Country/Territory	Singapore
City	Singapore
Period	23/2/5 → 23/2/8

Bibliographical note

Funding Information:
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) through Outdoor culture, Drone, Multi-sensing, Smart agriculture platform, Growth management project, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA)(322033-3)

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Electrical and Electronic Engineering
Control and Optimization

Access to Document

10.1109/ICEIC57457.2023.10049898

Cite this

Lee, S. E., Lee, S. H., & Kim, J. O. (2023). Deep-Clustering Based Plant Disease Segmentation Network. In 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIC57457.2023.10049898

Deep-Clustering Based Plant Disease Segmentation Network. / Lee, Seong Eui; Lee, Sang Ho; Kim, Jong Ok.
2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, SE, Lee, SH & Kim, JO 2023, Deep-Clustering Based Plant Disease Segmentation Network. in 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Singapore, Singapore, 23/2/5. https://doi.org/10.1109/ICEIC57457.2023.10049898

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title = "Deep-Clustering Based Plant Disease Segmentation Network",

abstract = "Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.",

author = "Lee, {Seong Eui} and Lee, {Sang Ho} and Kim, {Jong Ok}",

note = "Funding Information: This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) through Outdoor culture, Drone, Multi-sensing, Smart agriculture platform, Growth management project, funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA)(322033-3) Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 ; Conference date: 05-02-2023 Through 08-02-2023",

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AB - Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.

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M3 - Conference contribution

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ER -

Deep-Clustering Based Plant Disease Segmentation Network

Abstract

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Bibliographical note

ASJC Scopus subject areas

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