A Transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery

Abstract

Artificial intelligence (AI) and deep learning (DL) for plant disease detection are emerging research areas. DL methods generally require a large amount of annotated data for training, which is often costly, time-consuming, and infeasible. This article addresses the data scarcity problem and proposes a few-shot learning (FSL) method for barley plant disease detection. To prepare a dataset, we captured images from outdoor test-bed trials (at two different growth stages of plants across multiple paddocks) under various weather conditions, such as sunny and cloudy. The images are divided into patches and manually labelled into five classes: no-disease, net form net blotch (NFNB) (which is classified into two stages: early and severe), spot form net blotch (SFNB), and scald. We name this as the Barley dataset. We also used the publicly available cassava dataset, which has five classes. The datasets are then applied to the proposed FSL pipeline, which only uses as few as five images for each class in training. We use the Swin transformer as the network backbone. The method with the Swin-B variant as the feature extractor achieved a detection accuracy of 91.80% and 97.93% on the barley disease and cassava datasets, respectively. The result indicates that our FSL model can efficiently perform and classify barley disease with small training data.

Plant phenotyping relevance

圃場画像からオオムギ葉の病徴を検出・分類する少数ショット画像解析パイプラインを提案し、データセット上で精度検証しているため、植物表現型取得手法が中心である。

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