Exploring YOLOv8 architecture applications for weed detection in crops

Aleksandar Petrovic; Milos Pavkovic; Marina Svicevic; Nebojsa Budimirovic; Vuk Gajic; Dejan Jovanovic

doi:10.2991/978-94-6463-482-2_5

<Previous Article In Volume

Next Article In Volume>

Exploring YOLOv8 architecture applications for weed detection in crops

Authors

Aleksandar Petrovic¹^{, *}, Milos Pavkovic¹, Marina Svicevic²^{, 4}, Nebojsa Budimirovic¹, Vuk Gajic¹, Dejan Jovanovic³^{, 4}

¹Singidunum University, Faculty of Informatics and Computing, Danijelova 32, 11000, Belgrade, Serbia

²University of Kragujevac, Faculty of Science, Radoja Domanovića 12, 34000, Kragujevac, Serbia

³College of Academic Studies “Dositej”, Vojvode Putnika 7, 11000, Belgrade, Serbia

⁴Singidunum University, Belgrade, Serbia

^*Corresponding author. Email: aleksandar.petrovic@singidunum.ac.rs

Corresponding Author

Aleksandar Petrovic

Available Online 23 August 2024.

DOI: 10.2991/978-94-6463-482-2_5 How to use a DOI?
Keywords: YOLOv8; aerial imagery; weed detection; deep-learning
Abstract: This work has a goal to test a deep-learning approach to the problem of aerial weed detection in crops. The issue of this type of detection lies in the nature of plants and their life cycles. Crops as well as weeds change their appearance and can be similar in physical appearance. The use of advanced models like the You Only Look Once v8 (YOLOv8) allows for fast and accurate predictions. In this work, five different sizes of the YOLOv8 are applied to the same dataset consisting of aerial images of plants. The results, metrics, and actual predictions are provided for every of the five models. The modernization of the agricultural domain has begun, and the use of artificial intelligence (AI) is paramount to stay ahead of the competition. The experimental outcomes indicate significant potential of YOLO networks in this domain, and further possibility to integrate these networks with precision agriculture.
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 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
Series: Advances in Computer Science Research
Publication Date: 23 August 2024
ISBN: 978-94-6463-482-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-482-2_5 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  - Aleksandar Petrovic
AU  - Milos Pavkovic
AU  - Marina Svicevic
AU  - Nebojsa Budimirovic
AU  - Vuk Gajic
AU  - Dejan Jovanovic
PY  - 2024
DA  - 2024/08/23
TI  - Exploring YOLOv8 architecture applications for weed detection in crops
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 57
EP  - 73
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_5
DO  - 10.2991/978-94-6463-482-2_5
ID  - Petrovic2024
ER  -

download .riscopy to clipboard