基于深度学习的作物田间杂草识别研究进展

IF 2.2 2区 农林科学 Q2 AGRICULTURAL ENGINEERING International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI:10.25165/j.ijabe.20231604.8364
Wenze Hu, Samuel Oliver Wane, Junke Zhu, Dongsheng Li, Qing Zhang, Xiaoting Bie, Yubin Lan
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引用次数: 0

摘要

杂草的自动识别和检测是精确除草的关键。近年来,深度学习技术因其在作物杂草识别方面的潜力而受到广泛关注。本文综述了基于深度学习的作物田间杂草识别的研究现状和发展趋势。通过对国内外相关文献的分析,总结了基于dl的杂草鉴定技术的发展历史、研究进展以及鉴定检测方法。重点放在数据源和应用于不同技术任务的深度学习模型上。此外,本文还讨论了数据集准备耗时费力,通用性差,数据类别不平衡以及DL用于杂草识别的现场识别精度低的挑战。提出了相应的解决方案,为今后杂草鉴定的研究方向提供参考。关键词:深度学习,杂草检测,杂草分类,图像分割,卷积神经网络,图像处理DOI: 10.25165/ j.j ijabe.20231604.8364基于深度学习的作物田间杂草识别研究进展。农业与生物工程学报,2023;16(4): 1 - 10。
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Review of deep learning-based weed identification in crop fields
Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. Keywords: deep learning, weed detection, weed classification, image segmentation, Convolutional Neural Network, image processing DOI: 10.25165/j.ijabe.20231604.8364 Citation: Hu W Z, Wane S O, Zhu J K, Li D S, Zhang Q, Bie X T, et al. Review of deep learning-based weed identification in crop fields. Int J Agric & Biol Eng, 2023; 16(4): 1-10.
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来源期刊
CiteScore
4.30
自引率
12.50%
发文量
88
审稿时长
24 weeks
期刊介绍: International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.
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