subject: Agronomy subject: Remote Sensing type: dataset
10.4231/MY7W-FH43
Ayman F Habib , Ed Delp , Keith A Cherkauer , Larry L. Biehl , Melba M Crawford , Mitchell Tuinstra
05/27/2021
This publication includes sample rgb and hyperspectral image data collected in 2018 by unmanned aerial systems for a Sorghum Phenotying and Trait Analysis project being conducted at Purdue's Agronomy Center for Research and Education (ACRE).
Agronomy hyperspectral Phenotyping Remote Sensing Sorghum UAS
10.4231/69H7-CV75
Kai-Wei Yang , Mitchell Tuinstra , Scott Chapman
12/15/2020
A pipeline to generate the XML parameter file for APSIM was developed in R. The files and R codes are reported in "R Pipeline for Calculation of APSIM Parameters and Generating the XML File".
Agronomy APSIM Crop Growth Models APSIM Pipeline Remote Sensing
10.4231/KMK0-J993
Kai-Wei Yang , Mitchell Tuinstra , Scott Chapman
12/15/2020
The model calibration step compares the APSIM simulated results with measured phenotypes in field trials. Parameter adjustments are reported in “SorghumXMLOutputUQ”.
2018 Sorghum Simulation Agronomy APSIM Crop Model Remote Sensing
10.4231/0NX5-RT34
Kai-Wei Yang , Mitchell Tuinstra , Scott Chapman
12/15/2020
The APSIM models from 2018 West Lafayette were validated by comparing simulated and observed results of experiments conducted in 2015 West Lafayette.
2015 Sorghum Crop Simulation Agronomy Biophysical crop models Remote Sensing
10.4231/6NW4-TB31
Kai-Wei Yang , Mitchell Tuinstra , Scott Chapman
12/15/2020
The calibrated APSIM models from 2018 West Lafayette were validated by comparing simulated and observed results of experiments conducted in 2017 West Lafayette.
2017 Sorghum simulation Agronomy APSIM Crop Model Remote Sensing
10.4231/R76T0JK1
05/04/2015
Calibration data
Agronomy calibration Crop Science Crops Exotech 20C-SW LARS reflectance references Remote Sensing Soil Science solar illumination spectral observations spectrometer
10.4231/R7G73BMR
05/05/2015
Kathy Latz Soils Study
Agriculture Agronomy Exotech 20C-SW Laboratory for Applications of Remote Sensing (LARS) LARS Remote Sensing soil Soil Science
10.4231/FPHP-0153
Meng-yang Lin , Mitchell R Tuinstra
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
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