Subject
- Agriculture 121
- Soil Science 117
- Crop Science 95
- Agronomy 8
- Civil Engineering 2
Creator
- Craig S. T. Daughtry 46
- Marvin E. Bauer 34
- Larry L. Biehl, 0000-0002-2322-2445 9
- Mitchell Tuinstra, 0000-0002-5322-6519 5
- Barrett F. Robinson 4
- Kai-Wei Yang 4
- Scott Chapman 4
- Andrea Szilágyi Kishné 3
- Carlos R. Valenzuela 3
- Erika Csákiné Michéli 3
Type
- Dataset 133
Date
Total is 133 Results
Kansas Small Grains (771202)
10.4231/R7VH5KST
05/04/2015
The objective of this experiment was to determine if various small grains can be discriminated from each other based on their spectral reflectance.
Agriculture barley Crop Science Crops JSC FSAS LARS Remote Sensing rye Soil Science solar illumination spectral observations spectrometer triticale winter wheat
Kansas Other Crops (771204)
10.4231/R708638H
05/05/2015
Kansas Other Crops
Agriculture Alfalfa Crop Science Crops fallow LARS Remote Sensing Soil Science solar illumination spectral observations spectrometer
Modeling density surfaces of intraspecific classes using camera-trap distance sampling
10.4231/RF5H-C895
02/07/2023
Density surface modelling of intraspecific classes using camera-trap-distance-sampling data and hierarchical generalized additive modelling.
abundance deer density surface modelling Forestry and Natural Resources generalized additive model precision recruitment Remote Sensing ungulate
Row Selection in Remote Sensing for Maize and Sorghum
10.4231/PF9S-4G38
Mitchell R Tuinstra
,
Seth A Tolley
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
Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling
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
Indiana Statewide Digital Surface Model (2016-2019)
10.4231/0D14-5Q79
02/16/2021
Digital Surface Model generated from the Indiana Statewide LiDAR data (2016 - 2019)
Civil Engineering Data Science High Performance Computing LiDAR Remote Sensing
Indiana Statewide Normalized Digital Height Model (2016-2019)
10.4231/QAA5-6J29
02/16/2021
Normalized Digital Height Model generated from the Indiana Statewide LiDAR data (2016 - 2019)
Civil Engineering Data Science High Performance Computing LiDAR Remote Sensing
HTP-Soy: An Aerial Image Set of Multi-category Soybean for High-Throughput Phenotyping (HTP)
10.4231/ZAD3-MG98
Beichen Lyu
,
Katy Rainey
,
Keith Cherkauer
,
Stuart D Smith
01/06/2020
The publication “HTP-Soy” contains 1,728 aerial images of soybean plots in the field and each image is categorized based on spatial, temporal, and genetic variations. Data is made available to encourage development of HTP applications.
Agricultural and Biological Engineering Computer Vision High-throughput Phenotyping Image Dataset Indiana Plant Breeding Remote Sensing Soybean Unmanned Aerial Systems
Geospatial Image Data for Sorghum Phenotyping
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
R Pipeline for Calculation of APSIM Parameters and Generating the XML File
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
Display #
Results 121 - 130 of 133