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复制”，推动AI从实验室走向海洋牧场、走向渔港码头，也走向更广阔的市场。
记者 郑杨：我身后是青岛励图水产养殖AI系统，今年这套系统新增了体征识别等新功能，通过水下双目摄像头和声呐配合，系统就能数出塘里有多少
鱼、每条大概多大、多重，就连应该喂多少料都会自动算好，过去养鱼全靠老师傅“目测手感”，现在屏幕上一目了然，养多养少、喂多喂少，一套系统可以全部包办。
这套养殖AI系统能够如此进化，得益于青岛励图高科自主研

原文链接：https://m.thepaper.cn/newsDetail_forward_34293493

量发展的生动实践。乐东地处海南岛西南部，水质优良、气候适宜，是发展海水养殖的天然宝地。在乐东金瑞实业发展有限公司的养殖基地，标准化养
者展示了他们的“智慧养殖”新模式。水面之上光伏发电，水面之下工厂化养殖，AI智能管理与水质监测系统24小时不间断运转，实现了生态与效益的双
。乐东渔光互补生态水产有限公司负责人苏文师：我们产业园的工厂化养殖投入AI智能管理，还有水质监测以及光伏发电，大大减少了我们的用电成本，
本文作者：海南省农业农村厅

原文链接：http://m.toutiao.com/group/7687816354140996123/?upstream_biz=VolcEngine

ans. Despite significant strides in other sectors, the adaptation of HCI and ACI (collectively HACI) to farm animal welfare—particularly for dairy cows, swine, and poultry—lags behind. Our paper explores the potential of HACI within precision livestock farming (PLF) and artificial intelligence (AI) to enhance individual animal welfare and address the unique challenges within these settings. It underscores the necessity of transitioning from productivity-focused to animal-centered farming methods, advocating for a paradigm shift that emphasizes welfare as integral to sustainable farming pra

原文链接：https://pmc.ncbi.nlm.nih.gov/articles/PMC11604036/

South Dakota State University, Rapid City, SD, USA.
3 Chungnam National University, Daejeon, Korea.
PMID: 40808567
PMCID: PMC13057718
DOI: 10.5713/ab.25.0289
Abstract
Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies,

原文链接：https://pubmed.ncbi.nlm.nih.gov/40808567/

DIBAF, University of Tuscia, 01100 Viterbo, Italy.
6 Department of Agriculture, Food and Environment-DAFE, Università di Pisa, 56124 Pisa, Italy.
7 LIGHT Center Brescia, 25123 Brescia, Italy.
PMID: 39771778
PMCID: PMC11679443
DOI: 10.3390/s24248042
Abstract
We present an artificial intelligence (AI)-enhanced monitoring framework designed to assist personnel in evaluating and maintaining animal welfare using a modular architecture. This framework integrates multiple deep learning models to automatically compute metrics relevant to assessing animal well-being. Using deep learning for AI-based

原文链接：https://www.ncbi.nlm.nih.gov/sites/entrez/39771778

Enhanced Animal Behavioural Analytics For Improved Cattle Welfare, Health, Productivity and Sustainability
Lead Participant: QUANT FOUNDRY LIMITED
Overview
Organisations
People
Abstract
Funding
details
The Quant Foundry Artificial Intelligence (AI) Livestock Surveillance Solution in collaboration with Bristol Veterinary School at the University of Bristol aims to provide a world-class solution for the identification of anomalous cattle behaviour to aid in the rapid identification of different ailments. The solution combines AI-driven video analytics of animals within an automated farm framewo

原文链接：https://gtr.ukri.org/projects?ref=51618

Pawprints, pixels, and predictive, personalized care: how real-life data and AI are transforming veterinary medicine
Authors:
Janet Robishaw
PhD
Emailrobishawj@ufl.edu
and
Kelly Sobers
BS
Open access
Article Information
A New Direction in Veterinary Medicine at the University of Florida
At the University of Florida (UF) College of Veterinary Medicine (CVM), the future of animal care is being reshaped through a mix of data, technology, and collaboration. The goal is simple but ambitious: to improve the health of animals, people, and the environment by accessing big data, developing better tools

原文链接：https://avmajournals.avma.org/view/journals/ajvr/86/10/ajvr.25.08.0281.xml

e for alerting farmers to emerging disease outbreaks before clinical signs fully manifest. By leveraging subtle shifts in cow behavior, these models could lead to timely interventions that improve overall herd health and productivity.
As these data-driven methods proliferate, the role of generative AI has become a topic of both excitement and caution. One team posed the question: “Can ChatGPT diagnose my collapsing dog?” and delved into how large language models might intersect with veterinary triage or diagnostics (Abani, De Decker et al.). Their findings underscore the need for critical

原文链接：https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1607623/full

editing
Luis O Tedeschi : Writing – review & editing
Received 2025 Apr 17; Accepted 2025 Nov 8; Collection date 2026.
© The Author(s) 2025. Published by Oxford University Press on behalf of the American Society of Animal Science.
PMCID: PMC12875599 PMID: 41410516
Abstract
Artificial intelligence (AI) can transform livestock farming as producers start using data-driven decisions in key areas, such as animal health, reproduction, behavior, nutrition, and production management. This review examines how AI technologies, like machine learning, computer vision, and sensor-based systems, help monit

原文链接：https://pmc.ncbi.nlm.nih.gov/articles/PMC12875599/

iciency while also assuring animal welfare, environmental sustainability and economic viability. Precision livestock farming (PLF) has emerged as a transformative paradigm that integrates advanced sensing technologies, computer vision, internet of things infrastructures and artificial intelligence (AI) to enable continuous, automated and individualized animal monitoring. This paper explores the evolution of livestock management from conventional observation-based practices to sophisticated, data-driven architecture. It also synthesizes recent advancements in PLF emphasizing its system architec

原文链接：https://koreascience.kr/search.page?keywords=High-Efficiency&lang=ko&pageNo=1741&pageSize=10