An efficient non-parametric feature calibration method for 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.

Plant phenotyping relevance

植物病害画像分類のための特徴校正アルゴリズムとネットワーク構成を開発・評価しており、病害状態の推定手法が研究の中心である。

Code and data availability

The article uses public datasets (PlantVillage, PlantDoc, etc.) but provides no authors' public code, models, or paper-specific data deposits. The Data Availability Statement only offers further inquiries via the corresponding author, with no public URL or repository identifier anywhere in the supplied blocks.

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