Evidence map›Paper›PMID 42651937›Full record

ArticleAnimals : an open access journal from MDPI2026

Beef Cattle Body Weight Estimation Based on Dual-View RGB Images.

Ziruo Li, Yadan Zhang, Chong Yao, Ying Han, Zenglong Song, Xueting Zeng, Xiaocong Li, Gang Liu

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ziruo LiKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.ORCID 0009-0003-1662-3368
Yadan ZhangKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.ORCID 0000-0002-1818-1482
Chong YaoKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.
Ying HanKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.ORCID 0009-0006-1454-9071
Zenglong SongKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.
Xueting ZengKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.
Xiaocong LiKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.
Gang LiuKey Laboratory of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China.

Funding

National Key Research and Development Program of China 2024YFD1300603 and 2021YFD130050201
6 · The paper itself

Abstract

Non-contact body weight (BW) estimation provides a low-stress and low-cost approach for precision beef cattle management, but single-view RGB images may not fully capture body-shape information. This study proposed a practical dual-view RGB framework for cattle BW estimation. A total of 3210 paired top-view and side-view RGB images were collected from 107 Simmental beef cattle with BW ranging from 169 to 980 kg. An EMA-enhanced YOLO11n-seg model was adopted to improve cattle foreground extraction, and a two-stream CBAM-ResNet50-SE network was constructed to learn dorsal and lateral morphological features for BW regression. The EMA-YOLO11n-seg model achieved mAP@0.5 values of 99.18% and 98.35% for top-view and side-view images, respectively. On the test set, the proposed BW estimation model achieved an MAE of 14.96 kg, an RMSE of 17.86 kg, and an R

Indexed as

body weight estimationdual-view feature fusiondual-view RGB imagesinstance segmentationnon-contact measurement

Identifiers

PMID42651937
PMCPMC13508864

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