Evidence map›Paper›PMID 38338100›Full record

ArticleAnimals : an open access journal from MDPI2024

Research on Automatic Recognition of Dairy Cow Daily Behaviors Based on Deep Learning.

Rongchuan Yu, Xiaoli Wei, Yan Liu, Fan Yang, Weizheng Shen, Zhixin Gu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. DWEC-YOLO: A Multi-Behavior Detection Model for Group-Housed Pigs.Animals : an open access journal from MDPI · 2026
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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

6 authors.

Rongchuan YuCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Xiaoli WeiCollege of Electric and Information, Northeast Agricultural University, Harbin 150030, China.
Yan LiuCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Fan YangCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Weizheng ShenCollege of Electric and Information, Northeast Agricultural University, Harbin 150030, China.
Zhixin GuCollege of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Funding

National Key Research and Development Program of China 2022YFD1301104
6 · The paper itself

Abstract

Dairy cow behavior carries important health information. Timely and accurate detection of behaviors such as drinking, feeding, lying, and standing is meaningful for monitoring individual cows and herd management. In this study, a model called Res-DenseYOLO is proposed for accurately detecting the individual behavior of dairy cows living in cowsheds. Specifically, a dense module was integrated into the backbone network of YOLOv5 to strengthen feature extraction for actual cowshed environments. A CoordAtt attention mechanism and SioU loss function were added to enhance feature learning and training convergence. Multi-scale detection heads were designed to improve small target detection. The model was trained and tested on 5516 images collected from monitoring videos of a dairy cowshed. The experimental results showed that the performance of Res-DenseYOLO proposed in this paper is better than that of Fast-RCNN, SSD, YOLOv4, YOLOv7, and other detection models in terms of precision, recall, and mAP metrics. Specifically, Res-DenseYOLO achieved 94.7% precision, 91.2% recall, and 96.3% mAP, outperforming the baseline YOLOv5 model by 0.7%, 4.2%, and 3.7%, respectively. This research developed a useful solution for real-time and accurate detection of dairy cow behaviors with video monitoring only, providing valuable behavioral data for animal welfare and production management.

Indexed as

behavior detectiondairy cowdense modulemulti-scale detection headsYOLO

Identifiers

PMID38338100
PMCPMC10854845

What OpenQuestion holds

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Registered trials

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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.