推进精准畜牧业:整合人工智能和新兴技术实现可持续畜牧业管理。

IF 2.5 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Luis Orlindo Tedeschi, Pablo Guarnido Lopez, Hector M Menendez Iii, Seongwon Seo
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引用次数: 0

摘要

精准畜牧业(PLF)已经从基本的监测系统发展到复杂的人工智能(AI)驱动的决策支持系统,从而提高了牲畜管理效率、可持续性和动物福利。本文回顾了自2017年以来PLF的技术演变,重点介绍了传感技术、计算机视觉和人工智能方面的重大进展。包括RGB-D摄像头、3D成像系统和物联网平台在内的非侵入性技术现在可以实时捕获详细的生物特征和行为数据,而人工智能算法可以实现早期疾病检测、优化喂养策略和改善生殖管理。将这些技术与机械模型集成,创建了混合智能框架,解决了精确营养建模中长期存在的挑战。未来PLF的发展可能会集中在集成大型语言模型,采用联邦学习方法来解决数据隐私问题,以及为小规模生产者实现技术民主化。尽管技术取得了进步,但在数据标准化、农村环境的连通性、高实施成本以及围绕加强动物监测的伦理考虑等方面仍然存在挑战。通过促进动物科学家、工程师、计算机科学家和社会科学家之间的跨学科合作,PLF可以继续推动畜牧业生产的可持续和高效实践,同时确保技术补充而不是取代传统畜牧业知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing Precision Livestock Farming: Integrating Artificial Intelligence and Emerging Technologies for Sustainable Livestock Management.

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 artificial intelligence. Non-invasive technologies, including RGB-D cameras, 3D imaging systems, and IoT-enabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.

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来源期刊
Animal Bioscience
Animal Bioscience AGRICULTURE, DAIRY & ANIMAL SCIENCE-
CiteScore
5.00
自引率
0.00%
发文量
223
审稿时长
3 months
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