Evidence map›Paper›PMID 41472146›Full record

ArticleVeterinary sciences2025

Research on a Lightweight Recognition Model for Daily Cattle Behavior Toward Real-Time Monitoring.

Jianping Yao, Yong'an Zhang, Mei'an Li, Jia Li, Yanqiu Liu, Feilong Kang, Fan Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Veterinary sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jianping YaoCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Yong'an ZhangCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.ORCID 0000-0002-2731-2384
Mei'an LiCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Jia LiCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Yanqiu LiuCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Feilong KangCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Fan LiuCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.

Funding

Basic Re-search Funds Project for Directly Affiliated Universities in Inner Mongolia Autonomous Region BR230150First-Class Discipline Scientific Re-search Special Project of Inner Mongolia Autonomous Region YLXKZX-NND-057National Natural Science Foundation of China, grant number 32160813Natural Science Foundation of Inner Mongo-lia Autonomous Region Joint Project(2025LHMS06002) 2025LHMS06002Natural Science Foundation of Inner Mongolia Autonomous Region of China 2025LHMS03025Natural Science Foundation of Inner Mongolia Autonomous Region Pro-ject 2024MS03055
6 · The paper itself

Abstract

Accurate monitoring of cattle behavioral time budgets is crucial for early disease detection and welfare assessment. Changes in durations of standing, lying, and eating are known to be early indicators of health issues such as lameness and metabolic disorders. To enable low-cost, non-invasive, and real-time monitoring, this study proposes a lightweight cattle behavior recognition method based on an improved YOLO11n architecture. The model enhances multi-scale feature integration through a generalized efficient layer aggregation network (GELAN), improves feature extraction via a multidimensional collaborative attention (MCA) mechanism, and achieves efficient cross-scale fusion using a bidirectional feature pyramid network (BiFPN). Depthwise separable convolution (DWConv) is incorporated to reduce computational load. Experimental results demonstrate high recognition accuracy, with mAP@0.5 values of 91.2%, 91.0%, and 93.9% for standing, lying, and eating, respectively. The model was subsequently compressed using a Layer-adaptive Magnitude-based Pruning (LAMP) algorithm, resulting in a final model of only 1.06 × 10

Indexed as

animal welfarecattle behaviorcomputer visionedge computinglightweight model

Identifiers

PMID41472146
PMCPMC12737634

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.