DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease

Agronomy

Gianni Fenu¹  Francesca Maridina Malloci¹ 

[1] Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy;

关键词: plant disease prediction;    classification;    detection;    dataset;    survey;    machine learning;   

DOI  :  10.3390/agronomy11112107

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown