Datasets

subject: Sorghum subject: Remote Sensing type: dataset

Total is 11 Results
Kansas Intensive Test Site 1988 (771207)

10.4231/R7XG9P2F

Marvin E. Bauer

04/21/2015

The objective of this experiment was to provide a data set which can be used as an intermediate level of extrapolation between data collected from controlled experimental plots at field research stations and data collection by satellite scanners.

Agriculture Corn Crop Science Crops FSS LARS Remote Sensing soil Soil Science solar illumination Sorghum Soybeans spectral observations spectrometer winter wheat

Kansas Intensive Test Site 1988 (761207)

10.4231/R7251G4Z

Marvin E. Bauer

04/21/2015

The objective of this experiment was to provide a data set which can be used as an intermediate level of extrapolation between data collected from controlled experimental plots at field research stations and data collection by satellite scanners.

Agriculture Alfalfa Corn Crop Science Crops FSS Grass LARS Remote Sensing soil Soil Science solar illumination Sorghum Soybeans spectral observations spectrometer winter wheat

Kansas Intensive Test Site 1960 (751207)

10.4231/R7P26W11

Marvin E. Bauer

04/14/2015

The objective of this experiment was to provide a data set which can be used as an intermediate level of extrapolation between data collected from conrtolled experimental plots at field research stations and data collection by satellite scanners.

Agriculture Alfalfa Corn Crop Science Crops FSS Grass LARS Remote Sensing soil Soil Science solar illumination Sorghum Soybeans spectral observations spectrometer winter wheat

Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling

10.4231/FPHP-0153

Meng-yang Lin , Mitchell R Tuinstra ORCID logo

02/11/2022

High-throughput hyperspectral imaging in corn and sorghum can be used in multi-species models to predict water and nitrogen status of plants within and across these crop species.

Abiotic stress Agronomy Corn Ecophysiology High-throughput Phenotyping Machine Learning nitrogen content partial least square regression relative water content Remote Sensing Sorghum

Row Selection in Remote Sensing for Maize and Sorghum

10.4231/PF9S-4G38

Mitchell R Tuinstra ORCID logo , Seth A Tolley ORCID logo

07/26/2023

Remote sensing data evaluates all row segments of a plot, but the repeatability of traits from different row segments has not been evaluated. We evaluated which row segments provide the best repeatability and yield prediction of remote sensing traits.

Border effect High-throughput Phenotyping hyperspectral LiDAR Maize Plot trimming Predictive modelling Remote Sensing RGB Sorghum UAV

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Results 1 - 10 of 11