Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)

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Detection System of Cattle Foot and Mouth Disease (FMD) using Deep Learning

Authors
Moch. Zen Samsono Hadi1, *, Rizky Bintang Fahreza1, Dinda Dwimagfiroh1, Aries Pratiarso1, Haniah Mahmudah1
1Department of Electrical Engineering, Electronic Engineering PolytechnicInstitute of Surabaya (EEPIS), Surabaya, Jawa Timur, Indonesia
*Corresponding author. Email: zenhadi@pens.ac.id
Corresponding Author
Moch. Zen Samsono Hadi
Available Online 17 February 2024.
DOI
10.2991/978-94-6463-364-1_32How to use a DOI?
Keywords
cattle; illness; Foot and Mouth Disease (FMD); deep learning; YOLO
Abstract

Foot and Mouth Disease (FMD) is an extremely transmissible viral illness that specifically targets cloven-hoofed animals, such as cattle. It is caused by the Foot and Mouth Disease Virus (FMDV). Traditionally, FMD detection involves manual observation by trained veterinarians, which is time- consuming and subjective. The proposed system leverages the power of deep learning algorithms to automate the detection process, allowing for faster and more accurate FMD identification in cattle. In this research, we contrast various approaches for applying deep learning to diagnose Foot and Mouth Disease (FMD) in cattle. YOLOv4 and YOLOv4-tiny are the two algorithms that we concentrate on. By utilizing the FMD dataset to train each system, we can compare how effectively it performs to detect FMD in cattle. From the study we have done, a better accuracy was obtained in YOLOv4 with an accurate value of 98%. However, the detection speed of YOLOv4-tiny is much faster compared to Yolov4, but with a lower accuracy than YOLOV4.

Copyright
© 2024 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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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
Series
Advances in Engineering Research
Publication Date
17 February 2024
ISBN
10.2991/978-94-6463-364-1_32
ISSN
2352-5401
DOI
10.2991/978-94-6463-364-1_32How to use a DOI?
Copyright
© 2024 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  - Moch. Zen Samsono Hadi
AU  - Rizky Bintang Fahreza
AU  - Dinda Dwimagfiroh
AU  - Aries Pratiarso
AU  - Haniah Mahmudah
PY  - 2024
DA  - 2024/02/17
TI  - Detection System of Cattle Foot and Mouth Disease (FMD) using Deep Learning
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 339
EP  - 351
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_32
DO  - 10.2991/978-94-6463-364-1_32
ID  - Hadi2024
ER  -