ReviewSensors (Basel, Switzerland)2024
Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- Multi-Camera Analysis of Simulated Porcine Recovery States Based on Three-Dimensional Pose Using Synthetic Data.Journal of imaging · 2026Article
- Measurement of Shank Length in Live Chickens Using Visible-Infrared Image Fusion and Keypoint Prediction.Animals : an open access journal from MDPI · 2026Article
- Head-Body-Tail Segmentation of Poultry 3D Point Clouds Under Different Annotation Budgets: A Comparison of PCA-Based Rules and PointNet++-Based Methods.Animals : an open access journal from MDPI · 2026Article
- Advanced 3D Horse Reconstruction: Integrating Image-to-3D Modelling and Non-Rigid Registration for On-Barn Body Measurements.Animals : an open access journal from MDPI · 2026Article
- BoviFusionNet: A Lightweight Edge-Deployable AI System for Cattle Behavior Recognition in Livestock Monitoring.Veterinary sciences · 2026Article
- Recent Advances in Vision-Based Beef Cattle Body Measurement Technologies.Animals : an open access journal from MDPI · 2026Review
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- A fast and robust geometric point cloud registration model for orthopedic surgery with noisy and incomplete data.International journal of computer assisted radiology and surgery · 2025Article
- RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review.Journal of animal science and technology · 2025Review
- A Review of Visual Estimation Research on Live Pig Weight.Sensors (Basel, Switzerland) · 2024Review
- The Posture Detection Method of Caged Chickens Based on Computer Vision.Animals : an open access journal from MDPI · 2024Article
- Artificial Intelligence and Deep Learning in Sensors and Applications.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.
Indexed as
Identifiers
What OpenQuestion holds
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.