SynthesisSensors (Basel, Switzerland)2021
Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.
Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 176 citations in OpenAlex.
- Computer vision system for assessing pig welfare indicators on carcasses.Veterinary and animal science · 2026Article
- Associating panting levels of broiler breeders in a commercial battery cage barn with environmental and age factors via machine learning modeling.Poultry science · 2026Article
- DRG-LiteStar-YOLO: Reliability-Guided Pseudo-Depth Fusion for Dairy Goat Detection in Complex Barns.Animals : an open access journal from MDPI · 2026Article
- SowPostureDS: A Multi-Class Image Dataset for YOLO-Based Detection of Sow Postures in diverse Farrowing Systems.Scientific data · 2026Article
- AI-enabled predictive, preventive and personalised oral health management: a lightweight patient-centred model for automated assessment of dental plaque and gingival inflammation.The EPMA journal · 2026Article
- Evaluation of hybrid models based on image segmentation and inference for pig weight estimation.Frontiers in artificial intelligence · 2026Article
- CF-DETR: A Lightweight Real-Time Model for Chicken Face Detection in High-Density Poultry Farming.Animals : an open access journal from MDPI · 2025Article
- Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.Computers and electronics in agriculture · 2025Article
- A Novel Lightweight Dairy Cattle Body Condition Scoring Model for Edge Devices Based on Tail Features and Attention Mechanisms.Veterinary sciences · 2025Article
- Enhanced YOLOv8 for Robust Pig Detection and Counting in Complex Agricultural Environments.Animals : an open access journal from MDPI · 2025Article
- Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models.Poultry science · 2025Article
- Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring-A Critical Review.Animals : an open access journal from MDPI · 2025Review
- Multi-Modality Sheep Face Recognition Based on Deep Learning.Animals : an open access journal from MDPI · 2025Article
- Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.Scientific reports · 2025Article
- Dairy DigiD: a keypoint-based deep learning system for classifying dairy cattle by physiological and reproductive status.Frontiers in artificial intelligence · 2025Article
- RGB-based machine vision for enhanced pig disease symptoms monitoring and health management: a review.Journal of animal science and technology · 2025Review
- Benchmarking analysis of computer vision algorithms on edge devices for the real-time detection of digital dermatitis in dairy cows.Preventive veterinary medicine · 2024Article
- The analysis of art design under improved convolutional neural network based on the Internet of Things technology.Scientific reports · 2024Article
- Puzzle: taking livestock tracking to the next level.Scientific reports · 2024Article
- Machine Vision Analysis of Ujumqin Sheep's Walking Posture and Body Size.Animals : an open access journal from MDPI · 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
7 authors at 3 institutions in 1 country.
Funding
Abstract
Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.
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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.