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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Software survey: VOSviewer, a computer program for bibliometric mapping
van Eck, N. J., & Waltman, L. (2010) · Scientometrics · doi:10.1007/s11192-009-0146-3 · Cited by 21,484
Flagged first, then by citations
Research works printed but not linked: 7
- Aharon, 2021, Super-Gradients GitHub Repository
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., & Houlsby, N. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. · arXiv
- Girshick, 2015, Fast R-CNN.
- He, 2018, Mask R-CNN.
- Kumar, 2023, Drone-based apple detection: Finding the depth of apples using YOLOv7 architecture with multi-head attention mechanism Smart Agricultural, Technology, 5, 100311
- Terven, 2023, A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS Machine Learning and Knowledge, Extraction, 1680, 1716
- Wang, 2024, E-YOLO: Recognition of estrus cow based on improved YOLOv8n model Expert Syst, Appl, 238, 122212
Cited by
Sorted by citations
Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 528
Self-citationAli, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Gallagher, J., & Oughton, E. J. (2025) · IEEE Access · doi:10.1109/access.2025.3526458 · Cited by 157
YOLOv11 optimization for efficient resource utilization
Rasheed, A. F., & Zarkoosh, M. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07520-3 · Cited by 136
Khan, Z., Shen, Y., & Liu, H. (2025) · Agriculture · doi:10.3390/agriculture15131351 · Cited by 113
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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