Deep Potato

Published: 8 August 2022| Version 2 | DOI: 10.17632/xn2wy75f8m.2


Deep Potato - hyperspectral imagery of potato leaves with reference measurements dataset: towards potato physiological features modeling. The dataset contains 8 high-resolution hyperspectral orthophoto maps for deep learning modeling, stored in the "orthophoto_maps" folder. The maps are encoded in ENVI format. For each map 2 files: HDR file with the necessary information and BSQ file with imagery data are provided. The BSQ files are compressed as ZIP. - for reference, there are 60 lab measurements supplemented as well as 74 physiological measurements based on collected plants' material, both with precise geolocation data linking the measurements and the orthophoto maps. Those files are stored in the "measurements" folder, - hyperspectral images, extracted from orthophoto maps, stored in the "images" folder - you will find 84 different images there, stored in binary format (could be easily opened using python numpy library). - a "dataset_presentation.html" file with a brief presentation of the dataset. This data repository contains analytical data for the following submitted publication: - Ruszczak B., Boguszewska-Mańkowska D.: Deep Potato – the hyperspectral imagery of potato cultivation with reference agronomic measurements dataset: towards potato physiological features modeling, Data in Brief, 2022, 108087, ISSN 2352-3409,



Agronomy, Hyperspectral Imaging, Potato, Deep Learning, Hyperspectral Image Processing