The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Feeding behavior recognition for group-housed pigs with the Faster R-CNN
Abstract
Abstract The feeding behavior of each individual pig is an important indicator to determine whether it is healthy or not. Therefore, automatic behavior recognition for individual pig is one of the core problem in precision pig farming. Video surveillance is a common tool for monitoring animal behaviors. To accurately identify each pig from the video sequences is a prerequisite for individual pig behavior recognition. This paper proposed to use Faster R-CNN to locate and identify individual pigs from a group-housed pen. The head of each pig was also located. An algorithm for associating the head of each pig with its body was designed. On this basis, a behavior recognition algorithm based on feeding area occupation rate was implemented to measure the feeding behavior of pigs. Experiment showed that our algorithm can recognize the feeding behavior of pigs with a precision rate of 99.6% and recall rate of 86.93%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Cited by
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Yu, Y., Zhang, K., Yang, L., & Zhang, D. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.06.001 · Cited by 630
MacEachern, C. B., Esau, T. J., Schumann, A. W., Hennessy, P. J., & Zaman, Q. U. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100099 · Cited by 120
Metadata
- Authors
- Yang, Q., Xiao, D., & Lin, S.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 155
- Pages
- 453-460
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2899609705
- Cited by
- 182
- References
- 1
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Feeding behavior recognition for group-housed pigs with the Faster R-CNN. https://app.cochat.ai/papers/10.1016/j.compag.2018.11.002
- The paper
- Yang, Q., Xiao, D., & Lin, S. (2018). Feeding behavior recognition for group-housed pigs with the Faster R-CNN. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2018.11.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
