DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease

Abstract

The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.

Plant phenotyping relevance

植物病徴を画像で診断・モニタリングする公開データセットの構築と既存データセット比較が中心であり、植物病害状態の画像ベース表現型解析に該当する。

Code and data availability

The paper releases the DiaMOS Plant dataset (3505 field images of pear leaves/fruits with csv and YOLO annotations) on Zenodo, and the authors' LeafBox analysis code (used for the CNN classification benchmark) on GitHub. Both are paper-specific, public, and directly actionable.