Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications
Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition...
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| Format: | Article |
| Language: | English |
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Ankara University
2024-03-01
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| Series: | Journal of Agricultural Sciences |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/3315818 |
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| _version_ | 1849767483172454400 |
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| author | Sezai Ercisli Jingbin Li Kangle Song Yi Wang Huting Wang Yichen Yuan Jing Nie Yang Li |
| author_facet | Sezai Ercisli Jingbin Li Kangle Song Yi Wang Huting Wang Yichen Yuan Jing Nie Yang Li |
| author_sort | Sezai Ercisli |
| collection | DOAJ |
| description | Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected. |
| format | Article |
| id | doaj-art-4c5c9dca97b44804a52841a69265e067 |
| institution | DOAJ |
| issn | 1300-7580 2148-9297 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-4c5c9dca97b44804a52841a69265e0672025-08-20T03:04:11ZengAnkara UniversityJournal of Agricultural Sciences1300-75802148-92972024-03-0130221622810.15832/ankutbd.133951645Few-shot Learning in Intelligent Agriculture: A Review of Methods and ApplicationsSezai Ercisli0Jingbin Li1Kangle Song2Yi Wang3Huting Wang4Yichen Yuan5Jing Nie6Yang Li7ATATURK UNIVERSITYShihezi UniversityShihezi UniversityShihezi UniversityShihezi UniversityShihezi UniversityShihezi UniversityShihezi UniversityDue to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected.https://dergipark.org.tr/tr/download/article-file/3315818few-shot learningintelligent agriculturemeta-learningmetric learningfine-tunedata augmentation |
| spellingShingle | Sezai Ercisli Jingbin Li Kangle Song Yi Wang Huting Wang Yichen Yuan Jing Nie Yang Li Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications Journal of Agricultural Sciences few-shot learning intelligent agriculture meta-learning metric learning fine-tune data augmentation |
| title | Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications |
| title_full | Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications |
| title_fullStr | Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications |
| title_full_unstemmed | Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications |
| title_short | Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications |
| title_sort | few shot learning in intelligent agriculture a review of methods and applications |
| topic | few-shot learning intelligent agriculture meta-learning metric learning fine-tune data augmentation |
| url | https://dergipark.org.tr/tr/download/article-file/3315818 |
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