A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

Abstract

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

Plant phenotyping relevance

植物病斑画像を対象とした疾患識別・サブタイプ発見のための深層埋め込み画像クラスタリング手法を開発し、複数公開データセットで検証しているため、病害表現型の取得・解析が中心である。

Code and data availability

The paper's data availability statement provides public links to the raw plant lesion images (data.rar) and the authors' WDM-tSNE source code on GitHub, both paper-specific and directly actionable.