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A survey of few-shot learning in smart agriculture: developments, applications, and challenges
With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897954/ https://www.ncbi.nlm.nih.gov/pubmed/35248105 http://dx.doi.org/10.1186/s13007-022-00866-2 |
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author | Yang, Jiachen Guo, Xiaolan Li, Yang Marinello, Francesco Ercisli, Sezai Zhang, Zhuo |
author_facet | Yang, Jiachen Guo, Xiaolan Li, Yang Marinello, Francesco Ercisli, Sezai Zhang, Zhuo |
author_sort | Yang, Jiachen |
collection | PubMed |
description | With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans’ rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development. |
format | Online Article Text |
id | pubmed-8897954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88979542022-03-16 A survey of few-shot learning in smart agriculture: developments, applications, and challenges Yang, Jiachen Guo, Xiaolan Li, Yang Marinello, Francesco Ercisli, Sezai Zhang, Zhuo Plant Methods Review With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans’ rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development. BioMed Central 2022-03-05 /pmc/articles/PMC8897954/ /pubmed/35248105 http://dx.doi.org/10.1186/s13007-022-00866-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Yang, Jiachen Guo, Xiaolan Li, Yang Marinello, Francesco Ercisli, Sezai Zhang, Zhuo A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title | A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title_full | A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title_fullStr | A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title_full_unstemmed | A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title_short | A survey of few-shot learning in smart agriculture: developments, applications, and challenges |
title_sort | survey of few-shot learning in smart agriculture: developments, applications, and challenges |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897954/ https://www.ncbi.nlm.nih.gov/pubmed/35248105 http://dx.doi.org/10.1186/s13007-022-00866-2 |
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