Identification of Grass Weed Species Using YOLO5 Algorithm †
Abstract
1. Introduction
2. Methodology
2.1. Research Methodology
2.2. Hardware Development
2.3. Software Development
3. Experimental Set-Up
4. Results and Discussion
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Grass Weed Species | Data Collected |
---|---|
| 69 |
| 30 |
| 20 |
| 73 |
Total | 192 |
N = 192 | Identification | ||||
---|---|---|---|---|---|
Weed A | Weed B | Weed C | Weed D | ||
Ground truth | Weed A | 68 | 1 | ||
Weed B | 29 | 1 | |||
Weed C | 6 | 14 | |||
Weed D | 1 | 72 |
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Rabulan, C.G.; Gascon, J.A.; Linsangan, N. Identification of Grass Weed Species Using YOLO5 Algorithm. Eng. Proc. 2025, 92, 86. https://doi.org/10.3390/engproc2025092086
Rabulan CG, Gascon JA, Linsangan N. Identification of Grass Weed Species Using YOLO5 Algorithm. Engineering Proceedings. 2025; 92(1):86. https://doi.org/10.3390/engproc2025092086
Chicago/Turabian StyleRabulan, Charlene Grace, John Alfred Gascon, and Noel Linsangan. 2025. "Identification of Grass Weed Species Using YOLO5 Algorithm" Engineering Proceedings 92, no. 1: 86. https://doi.org/10.3390/engproc2025092086
APA StyleRabulan, C. G., Gascon, J. A., & Linsangan, N. (2025). Identification of Grass Weed Species Using YOLO5 Algorithm. Engineering Proceedings, 92(1), 86. https://doi.org/10.3390/engproc2025092086