Datasets

subject: Agronomy subject: Remote Sensing type: dataset

Total is 8 Results
Geospatial Image Data for Sorghum Phenotyping

10.4231/MY7W-FH43

Ayman F Habib ORCID logo , Ed Delp ORCID logo , Keith A Cherkauer ORCID logo , Larry L. Biehl ORCID logo , Melba M Crawford , Mitchell Tuinstra ORCID logo

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

R Pipeline for Calculation of APSIM Parameters and Generating the XML File

10.4231/69H7-CV75

Kai-Wei Yang , Mitchell Tuinstra ORCID logo , 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

2018 West Lafayette Simulation of 18 Sorghum Hybrids

10.4231/KMK0-J993

Kai-Wei Yang , Mitchell Tuinstra ORCID logo , 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

2015 West Lafayette Simulation of 18 Sorghum Hybrids

10.4231/0NX5-RT34

Kai-Wei Yang , Mitchell Tuinstra ORCID logo , 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

2017 West Lafayette Simulation of 18 Sorghum Hybrids

10.4231/6NW4-TB31

Kai-Wei Yang , Mitchell Tuinstra ORCID logo , 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

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

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