The datasets provide hyperspectral imagery of potato fields with referencing agronomic measurements of several parameters. It contains also meteorological data collected on the place at the same time and some additional data on the variety of potatoes and the experiment. The experiment has been conducted in 2020 and two different potato varieties (Lady Claire and Markies) on the different soil profiles were planted and observed. During that time, on 4 different days, to provide a detailed picture of the experiment the hyperspectral imagery has been taken using a UAV and 150-band hyperspectral camera. The collected material has been later processed into 8 georeferenced ortophotomaps. To provide precise reference information that could be later used for modeling purpose the measurements of plants from each field has been performed. The registered data contains data on plant height, number of stems, stem fresh and dry mass, leaf fresh and dry mass, leaf assimilation area and LAI, number of tubers, tuber fresh and dry mass, starch content, RWC, chlorophyll a fluorescence index, the maximum quantum yield of PSII photochemistry, and the performance of electron flux.
Keywords: Deep learning; Hyperspectral imagery (HSI); Image processing; Potato agronomic measurements; Potato physiology; Soil moisture.
© 2022 The Authors.