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
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
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
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
Spectral Separability of Wheat and Other Cover Crops in North Dakota (771215)
10.4231/R79P2ZK9
04/21/2015
The purpose of this experiment is to determine the spectral separability of spring wheat and other cover types common to western North Dakota.
Agriculture Crop Science Crops Exotech 100 Exotech 20C-SW LARS radiometer Remote Sensing soil Soil Science solar illumination spectral observations spectrometer winter wheat
Small Grains North Dakota (771212)
10.4231/R7TM7825
04/21/2015
The objective of this experiment is to determine if various small grains can be discriminated from each other based on their spectral reflectance.
Agriculture barley Crop Science Crops durum Exotech 100 Exotech 20C-SW LARS oats radiometer Remote Sensing Soil Science solar illumination spectral observations spectrometer spring wheat
Spring Wheat North Dakota (771213)
10.4231/R7K64G1R
04/21/2015
The objective of this factorial experiment was to quantify the effects on spectral response of the major variables affecting wheat growth, development and yield.
Agriculture Crop Science Crops Exotech 100 Exotech 20C-SW LARS radiometer Remote Sensing Soil Science solar illumination spectral observations spectrometer spring wheat
Display #
Results 111 - 120 of 133