利用人工智能增强农业食品系统的能力:进展、挑战和机遇概览

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Tao Chen, Liang Lv, Di Wang, Jing Zhang, Yue Yang, Zeyang Zhao, Chen Wang, Xiaowei Guo, Hao Chen, Qingye Wang, Yufei Xu, Qiming Zhang, Bo Du, Liangpei Zhang, Dacheng Tao
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引用次数: 0

摘要

随着世界人口的快速增长,改造我们的农粮系统,使其更具生产力、效率、安全性和可持续性,对于缓解潜在的粮食短缺问题至关重要。最近,深度学习(DL)等人工智能(AI)技术已在语言、视觉、遥感(RS)和农业食品系统应用等多个领域展现出强大的能力。然而,人工智能对农粮系统的总体影响仍不明确。在本文中,我们将深入探讨人工智能技术如何改变农业食品系统,并为现代农业食品工业做出贡献。首先,我们总结了农业食品系统中的数据采集方法,包括采集、存储和处理技术。其次,我们对农粮系统中的人工智能方法进行了进展回顾,特别是在农业、畜牧业和渔业方面,涵盖了农粮分类、生长监测、产量预测和质量评估等主题。此外,我们还强调了利用人工智能改造现代农业食品系统的潜在挑战和有前途的研究机会。我们希望这份调查报告能够为这一领域的新手提供一个全面的了解,并作为他们进一步研究的起点。项目网站:https://github.com/Frenkie14/Agrifood-Survey。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages. Recently, artificial intelligence (AI) techniques such as deep learning (DL) have demonstrated their strong abilities in various areas, including language, vision, remote sensing (RS), and agrifood systems applications. However, the overall impact of AI on agrifood systems remains unclear. In this paper, we thoroughly review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry. Firstly, we summarize the data acquisition methods in agrifood systems, including acquisition, storage, and processing techniques. Secondly, we present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery, covering topics such as agrifood classification, growth monitoring, yield prediction, and quality assessment. Furthermore, we highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI. We hope this survey could offer an overall picture to newcomers in the field and serve as a starting point for their further research. The project website is https://github.com/Frenkie14/Agrifood-Survey.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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