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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8980707 | PMC |
| http://dx.doi.org/10.1016/j.dib.2022.108087 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluation of Band Selection for Spectrum-Aided Visual Enhancer (SAVE) for Esophageal Cancer Detection.
J Cancer
January 2025
Department of Mechanical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan.
Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. In medical imaging, narrow-band imaging (NBI) is an imaging technique for endoscopic diagnostic medical tests, where light of specific blue and green wavelengths is used to enhance the detail of certain aspects of the surface of the mucosa.
View Article and Find Full Text PDFEvaluation of atmospheric correction algorithms for salt lake water assessment: Accuracy, band-specific effects, and sensor consistency.
PLoS One
December 2024
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa.
Atmospheric correction plays an important role in satellite monitoring of lake water quality. However, different atmospheric correction algorithms yield significantly different accuracy for inland lake waters beset by shallowness and turbidity. Finding a suitable algorithm for a specific lake is critical for quantitative satellite water-environmental monitoring.
View Article and Find Full Text PDFAboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass.
View Article and Find Full Text PDFPrediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery.
Plant Methods
December 2024
CAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016, China.
Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var.
View Article and Find Full Text PDFEstimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images.
Front Plant Sci
November 2024
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is a crucial parameter for evaluating crop growth and informing fertilization management in agricultural fields. Compared to traditional methods, UAV-based hyperspectral imaging technology offers significant advantages for non-destructive, rapid monitoring of crop LAI by simultaneously capturing both spectral information and two-dimensional images of the crop canopy, which reflect changes in its structure. While numerous studies have demonstrated that various texture features, such as the Gray-Level Co-occurrence Matrix (GLCM), can be used independently or in combination with crop canopy spectral data for LAI estimation, limited research exists on the application of Haralick textures for evaluating crop LAI across multiple growth stages.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!