The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review
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“This real-time capability is crucial in mitigating the spread of fires and protecting valuable forest and agricultural resources [111].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [111]
In the citing paper
“The system outperformed conventional models, achieving an F1-score of 93% and mAP of 85.8%.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [111]
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Metadata
- Authors
- Badgujar, C. M., Poulose, A., & Gan, H.
- Affiliations
- University of Tennessee at Knoxville (US)
- Indian Institute of Science Education and Research Thiruvananthapuram (IN)
- Corresponding author
- Badgujar, Chetan M
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 223
- Pages
- 109090
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W4399235601
- Cited by
- 411
- References
- 91
- Citations by year
- 2026: 167 · 2025: 203 · 2024: 41
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers and Electronics in Agriculture (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- YOLO, object detection, computer vision for agriculture, digital agriculture, one-stage object detection, real-time detection, agricultural monitoring, agricultural robotics, research trends
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