A survey of few-shot learning in smart agriculture: developments, applications, and challenges release_oeip3xmdk5d3bdoki6otzpvsha

by Jiachen Yang, Xiaolan Guo, Yang Li, Francesco Marinello, Sezai Ercisli, Zhuo Zhang

Published in Plant Methods by Springer Science and Business Media LLC.

2022   Volume 18, Issue 1, p28

Abstract

<jats:title>Abstract</jats:title>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.
In application/xml+jats format

Archived Files and Locations

application/pdf  1.9 MB
file_wpxhbv5ik5h5zesg6fweo7myoi
plantmethods.biomedcentral.com (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2022-03-05
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  1746-4811
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: faa59a2d-8624-49f3-b52d-c02c7acb061b
API URL: JSON