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

## TL;DR

This paper introduces a new method for accurately classifying plant diseases with limited data using a feature calibration approach.

## Contribution

The novel contribution is the Plant Disease Feature Calibration (PDFC) algorithm and the use of the FAS metric for efficient few-shot learning.

## Key findings

- The Swin-Transformer V2-F6 network structure is suitable for few-shot plant disease classification without training.
- The PDFC algorithm improves classification accuracy by calibrating features from different datasets.
- The proposed method outperforms existing state-of-the-art models in few-shot plant disease classification.

## 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.