An efficient non-parametric feature calibration method for few-shot plant disease classification
The temporal and spatial irregularity of plant diseases results in insufficient image data for certain diseases, challenging traditional deep learning methods that rely on large amounts of manually annotated data for training. Few-shot learning has emerged as an effective solution to this problem. T...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1541982/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733690580533248 |
|---|---|
| author | Jiqing Li Zhendong Yin Dasen Li Hongjun Zhang Mingdong Xu |
| author_facet | Jiqing Li Zhendong Yin Dasen Li Hongjun Zhang Mingdong Xu |
| author_sort | Jiqing Li |
| collection | DOAJ |
| description | The temporal and spatial irregularity of plant diseases results in insufficient image data for certain diseases, challenging traditional deep learning methods that rely on large amounts of manually annotated data for training. Few-shot learning has emerged as an effective solution to this problem. This paper proposes a method based on the Feature Adaptation Score (FAS) metric, which calculates the FAS for each feature layer in the Swin-TransformerV2 structure. By leveraging the strict positive correlation between FAS scores and test accuracy, we can identify the Swin-Transformer V2-F6 network structure suitable for few-shot plant disease classification without training the network. Furthermore, based on this network structure, we designed the Plant Disease Feature Calibration (PDFC) algorithm, which uses extracted features from the PlantVillage dataset to calibrate features from other datasets. Experiments demonstrate that the combination of the Swin-Transformer V2F6 network structure and the PDFC algorithm significantly improves the accuracy of few-shot plant disease classification, surpassing existing state-of-the-art models. Our research provides an efficient and accurate solution for few-shot plant disease classification, offering significant practical value. |
| format | Article |
| id | doaj-art-a46af6f54bb6408ab520175f9327736f |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-a46af6f54bb6408ab520175f9327736f2025-08-20T03:07:58ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15419821541982An efficient non-parametric feature calibration method for few-shot plant disease classificationJiqing LiZhendong YinDasen LiHongjun ZhangMingdong XuThe temporal and spatial irregularity of plant diseases results in insufficient image data for certain diseases, challenging traditional deep learning methods that rely on large amounts of manually annotated data for training. Few-shot learning has emerged as an effective solution to this problem. This paper proposes a method based on the Feature Adaptation Score (FAS) metric, which calculates the FAS for each feature layer in the Swin-TransformerV2 structure. By leveraging the strict positive correlation between FAS scores and test accuracy, we can identify the Swin-Transformer V2-F6 network structure suitable for few-shot plant disease classification without training the network. Furthermore, based on this network structure, we designed the Plant Disease Feature Calibration (PDFC) algorithm, which uses extracted features from the PlantVillage dataset to calibrate features from other datasets. Experiments demonstrate that the combination of the Swin-Transformer V2F6 network structure and the PDFC algorithm significantly improves the accuracy of few-shot plant disease classification, surpassing existing state-of-the-art models. Our research provides an efficient and accurate solution for few-shot plant disease classification, offering significant practical value.https://www.frontiersin.org/articles/10.3389/fpls.2025.1541982/fulldeep learningfew-shot learningplant disease classificationfeature calibrationimage classification |
| spellingShingle | Jiqing Li Zhendong Yin Dasen Li Hongjun Zhang Mingdong Xu An efficient non-parametric feature calibration method for few-shot plant disease classification Frontiers in Plant Science deep learning few-shot learning plant disease classification feature calibration image classification |
| title | An efficient non-parametric feature calibration method for few-shot plant disease classification |
| title_full | An efficient non-parametric feature calibration method for few-shot plant disease classification |
| title_fullStr | An efficient non-parametric feature calibration method for few-shot plant disease classification |
| title_full_unstemmed | An efficient non-parametric feature calibration method for few-shot plant disease classification |
| title_short | An efficient non-parametric feature calibration method for few-shot plant disease classification |
| title_sort | efficient non parametric feature calibration method for few shot plant disease classification |
| topic | deep learning few-shot learning plant disease classification feature calibration image classification |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1541982/full |
| work_keys_str_mv | AT jiqingli anefficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT zhendongyin anefficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT dasenli anefficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT hongjunzhang anefficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT mingdongxu anefficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT jiqingli efficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT zhendongyin efficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT dasenli efficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT hongjunzhang efficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification AT mingdongxu efficientnonparametricfeaturecalibrationmethodforfewshotplantdiseaseclassification |