ArticleVeterinary sciences2025
A Novel Lightweight Dairy Cattle Body Condition Scoring Model for Edge Devices Based on Tail Features and Attention Mechanisms.
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 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- UGT-YOLO: A Multi-Strategy Fusion Model for Automated Dairy Cow Body Condition Scoring.Animals : an open access journal from MDPI · 2026Article
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Machine-learning-based quantitative body condition scoring in Qinchuan beef cattle using conventional body measurements.Tropical animal health and production · 2026Article
- CSAG-DETR: a lightweight detector for accurate weed detection in sugar beet fields under complex field conditions.Frontiers in plant science · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The Body Condition Score (BCS) is a key indicator of dairy cattle's health, production efficiency, and environmental impact. Manual BCS assessment is subjective and time-consuming, limiting its scalability in precision agriculture. This study utilizes computer vision to automatically assess cattle body condition by analyzing tail features, categorizing BCS into five levels (3.25, 3.50, 3.75, 4.0, 4.25). SE attention improves feature selection by adjusting channel importance, while spatial attention enhances spatial information processing by focusing on key image regions. EfficientNet-B0, enhanced by SE and spatial attention mechanisms, improves feature extraction and localization. To facilitate edge device deployment, model distillation reduces the size from 23.8 MB to 8.7 MB, improving inference speed and storage efficiency. After distillation, the model achieved 91.10% accuracy, 91.14% precision, 91.10% recall, and 91.10% F1 score. The accuracy increased to 97.57% for ±0.25 BCS error and 99.72% for ±0.5 error. This model saves space and meets real-time monitoring requirements, making it suitable for edge devices with limited resources. This research provides an efficient, scalable method for automated livestock health monitoring, supporting intelligent animal husbandry development.
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Registered trials
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.