Polder, G. (Gerrit); van Marrewijk, B. M. (Bart); P.M. (Pieter) Blok; de Villiers, H.A.C. (Hendrik); van der Wolf, J.M. (Jan) et. al. (2020): Data underlying the publication: Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:b1f7853c-f52b-4f33-bb06-a6539c7a45a4

Dataset

• Artificial Intelligence and Image Processing
• Agriculture, Land and Farm Management

Disease detection, Erwinia, hyperspectral imaging, imaging spectroscopy, potato plants, Potato Y virus

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2017-06-27/2017-07-03

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by G. (Gerrit) Polder , B. M. (Bart) van Marrewijk , P.M. (Pieter) Blok , H.A.C. (Hendrik) de Villiers , J.M. (Jan) van der Wolf, J. (Jan) Kamp, T. (Toon) Tielen , G.A.L.M. (Gert-Jan) Swinkels, A. (Angelo) Mencarelli , K. (Kees) Boons, Y. (Youri) Schoutsen

Bacterial and virus diseases causes major damage in agriculture. Classifying those is an challenging but important task. This dataset contains +-274 000 spectral line images (512 pixels x 56 bands) of 6 different cultivars of healthy, bacterial (Erwinia) and virus (PVY) infected plants. The coordinates of each image is stored and can be compared with the stored position of individual plants labeled by crop experts.

• 2020-06-16 first online, published, posted

4TU.Centre for Research Data

media types: application/octet-stream, application/vnd.google-earth.kml+xml, application/x-7z-compressed, image/png, image/tiff, text/plain

• https://doi.org/10.3389/fpls.2019.00209

• Dutch Topsector Agri&Food. Op naar precisielandbouw 2.0, AF-14275

Wageningen University and Research