An efficient non-parametric feature calibration method for few-shot plant disease classification.

Journal: Frontiers in plant science
Published Date: May 19, 2025

Abstract

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.

Authors

  • Jiqing Li
    Department of Emergency Medicine Qilu Hospital of Shandong University Jinan China; Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine Institute of Emergency and Critical Care Medicine of Shandong University Chest Pain Center Qilu Hospital of Shandong University Jinan China; Key Laboratory of Emergency and Critical Care Medicine of Shandong Province Key Laboratory of Cardiopulmonary-Cerebral Resuscitation Research of Shandong Province Shandong Provincial Engineering Laboratory for Emergency and Critical Care Medicine Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging Qilu Hospital of Shandong University Jinan China.
  • Zhendong Yin
    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
  • Dasen Li
    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
  • Hongjun Zhang
    Ministry of Agriculture, Institute for the Control of Agrochemicals, No. 22 Maizidian Street, Beijing 110000, China.
  • Mingdong Xu
    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.

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