Deep learning instance level segmentation of tomato sepals on hyperspectral images

Željana Grbović, Marko Panić, Sanja Brdar, E.M. Hogeveen-van Echtelt, M.G.J. Mensink, E.J. Woltering, A. Chauhan

Research output: Chapter in Book/Report/Conference proceedingAbstract

Abstract

Tomatoes are extremely prone to pathogenic fungi infections. During product storage, inadequate temperature and humidity conditions can create ideal settings for the fungi to germinate. The most fragile parts of the tomatoes are the sepals, especially on their tips, which are the main entrance spots for fungal spores. Segmentation of tomato sepals in hyperspectral images is an essential step in designing automated systems for fungal infection sensitivity assessment. Precise segmentation of sepals could contribute to predictive models for early assessment of the risk of undesired fungal occurrence.
In this study, we analyze the state-of-the-art deep learning architectures for instance segmentation to identify which architecture provides the most precise tomato sepal segmentation in hyperspectral images.
Original languageEnglish
Title of host publicationIPPS 2022 Conference Book
Subtitle of host publication7th International Plant Phenotyping Symposium ‘Plant Phenotyping for a Sustainable Future'
PublisherWageningen University & Research
Pages239-239
DOIs
Publication statusPublished - 26 Sept 2022
Event7th International Plant Phenotyping Symposium: Plant Phenotyping for a Sustainable Future - Wageningen, Netherlands
Duration: 27 Sept 202230 Sept 2022

Conference

Conference7th International Plant Phenotyping Symposium
Abbreviated titleIPPS 2022
Country/TerritoryNetherlands
CityWageningen
Period27/09/2230/09/22

Fingerprint

Dive into the research topics of 'Deep learning instance level segmentation of tomato sepals on hyperspectral images'. Together they form a unique fingerprint.

Cite this