Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)

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YOLOv8n-seg for plants disease Detection and Instance Segmentation

Authors
Mahamed Abdelmadjid Allali1, 2, *, Nassima Bousahba3, 4, Hanaa Hadj Kaddour4, Asma Nedjari4, Halla Guetarni4
1SIMPA Laboratory, Mohamed Boudiaf University, Usto, Oran, Algeria
2LIO Laboratory, Hassiba Benbouali University of Chlef, Ouled Fares, Algeria
3EEDIS Laboratory, Djillali Liabes University, Sidi Bel Abbes, Algeria
4Computer Science Department, Hassiba Benbouali University of Chlef, Ouled Fares, Algeria
*Corresponding author. Email: m.allali@univ-chlef.dz
Corresponding Author
Mahamed Abdelmadjid Allali
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-496-9_5How to use a DOI?
Keywords
plant disease detection; instance segmentation; Yolov8; computer vision
Abstract

Climate change, the agricultural industry, and a nation’s economy all heavily rely on plants. Hence, the process of tending to plant assumes significant importance. Just as humans, plants are susceptible to various diseases caused by bacteria, fungi, and viruses. Timely identification and subsequent treatment of these diseases are crucial to prevent the complete destruction of the entire plant. This study presents a novel approach for plant disease detection utilizing a YOLOv8n-seg model with instance segmentation. The proposed model was trained using a combined total of 6,970 manually annotated images from two datasets. The plant disease detection and segmentation models proposed in this study demonstrate a mean average precision (mAP) of 98.34%, surpassing the performance of benchmark state-of-the-art models.

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 Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series
Advances in Intelligent Systems Research
Publication Date
31 August 2024
ISBN
978-94-6463-496-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-496-9_5How 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  - Mahamed Abdelmadjid Allali
AU  - Nassima Bousahba
AU  - Hanaa Hadj Kaddour
AU  - Asma Nedjari
AU  - Halla Guetarni
PY  - 2024
DA  - 2024/08/31
TI  - YOLOv8n-seg for plants disease Detection and Instance Segmentation
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 50
EP  - 62
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_5
DO  - 10.2991/978-94-6463-496-9_5
ID  - Allali2024
ER  -