Deep Metric Learning-Based Strawberry Disease Detection With Unknowns.

Abstract

There has been substantial research that has achieved significant advancements in plant disease detection based on deep object detection models. However, with unknown diseases, it is difficult to find a practical solution for plant disease detection. This study proposes a simple but effective strawberry disease detection scheme with unknown diseases that can provide applicable performance in the real field. In the proposed scheme, the known strawberry diseases are detected with deep metric learning (DML)-based classifiers along with the unknown diseases that have certain symptoms. The pipeline of our proposed scheme consists of two stages: the first is object detection with known disease classes, while the second is a DML-based post-filtering stage. The second stage has two different types of classifiers: one is softmax classifiers that are only for known diseases and the K -nearest neighbor ( K -NN) classifier for both known and unknown diseases. In the training of the first stage and the DML-based softmax classifier, we only use the known samples of the strawberry disease. Then, we include the known ( a priori ) and the known unknown training samples to construct the K -NN classifier. The final decisions regarding known diseases are made from the combined results of the two classifiers, while unknowns are detected from the K -NN classifier. The experimental results show that the DML-based post-filter is effective at improving the performance of known disease detection in terms of mAP. Furthermore, the separate DML-based K -NN classifier provides high recall and precision for known and unknown diseases and achieve 97.8% accuracy, meaning it could be exploited as a Region of Interest (ROI) classifier. For the real field data, the proposed scheme achieves a high mAP of 93.7% to detect known classes of strawberry disease, and it also achieves reasonable results for unknowns. This implies that the proposed scheme can be applied to identify disease-like symptoms caused by real known and unknown diseases or disorders for any kind of plant.

Plant phenotyping relevance

イチゴの病徴を画像から検出・分類する手法の開発と実フィールドでの性能評価が研究の中心であり、植物の病害状態を直接推定するため。

Code and data availability

The supplied blocks describe a self-constructed strawberry disease image dataset (7,230 images) and a DML-based detection pipeline, but contain no public deposit, availability URL, or code release for the dataset, images, models, or analysis code. The 'Data availability statement' section is listed in the outline but其s

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