Datasets

Subject
Creator
Type
Date

subject: Agricultural and Biological Engineering type: dataset date: 2020

Total is 7 Results
Raman spectra-based deep learning – A tool to identify microbial contamination

10.4231/HQGB-K827

Amir Hossein Raffiee , Arezoo M. Ardekani , Denilson Mendes De Oliveira ORCID logo , Mohit S. Verma ORCID logo , Murali K. Maruthamuthu ORCID logo

09/03/2020

Raman spectroscopic dataset for the potential microbes contaminating the pharmaceutical industry.

Agricultural and Biological Engineering deep learning microbial contamination Raman

HTP-Soy: An Aerial Image Set of Multi-category Soybean for High-Throughput Phenotyping (HTP)

10.4231/ZAD3-MG98

Beichen Lyu , Katy Rainey ORCID logo , Keith Cherkauer ORCID logo , 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

Arequipa Climate Maps - Yearly, Monthly and Daily

10.4231/Y00Z-N127

Andre Geraldo Moraes , Carlos Zeballos Velarde ORCID logo , Keith A Cherkauer ORCID logo , Laura C Bowling ORCID logo

05/29/2020

Arequipa Climate Maps – Yearly, Monthly and Daily (ACM-YMD), contains 30 years (1988-2017) of annual, monthly and daily gridded climate maps based on daily precipitation, and maximum and minimum air temperature for the Arequipa Department in Peru.

Agricultural and Biological Engineering Arequipa Arequipa Nexus Institute Climate Dynamics Climate Variability Climatology Geospatial Modeling Peru Precipitation Regression kriging Spatial Analysis Spatial modeling Temperature

Estimated land cover properties for hydrologic modeling in Arequipa, Peru

10.4231/GYN8-W684

Fariborz Daneshvar , Jane R Frankenberger ORCID logo , Johann Alexander Vera Mercado , Keith A Cherkauer ORCID logo , Laura C Bowling ORCID logo

12/03/2020

This dataset provides spatially-distributed land cover properties required for SWAT hydrologic modeling in the Arequipa Department of Peru. Developed methodology for estimation of land cover properties is also provided.

Agricultural and Biological Engineering ArcGIS Arequipa Arequipa Nexus Institute Crops Geographic Information Systems (GIS) grassland Hydrologic Modeling Hydrology Peru SWAT model UNSA Watershed

Estimated soil physical properties for hydrologic modeling in Arequipa, Peru

10.4231/N5MX-9W30

Fariborz Daneshvar , Jane R Frankenberger ORCID logo , Keith A Cherkauer ORCID logo , Laura C Bowling ORCID logo , Martín Juan C. Villalta Soto

10/30/2020

This dataset provides spatially-distributed soil physical properties required for SWAT hydrologic modeling in the Arequipa Department of Peru, based on regional soil maps and available soil profiles. Developed methodology is also provided.

Agricultural and Biological Engineering ArcGIS Arequipa Arequipa Nexus Institute Digital Soil Mapping Geographic Information Systems (GIS) Hydrologic Modeling Hydrology Peru soil SWAT model UNSA Watershed

A Rainfall-based, Sequential Depression-filling Algorithm and Applications on a Watershed in Northeastern Indiana, USA

10.4231/S2B6-J628

Dennis Buckmaster , Samuel Adam Noel ORCID logo

10/30/2020

Contents include script files for a sequential depression-filling algorithm developed in Matlab as well as elevation datasets used in a manuscript publication.

Agricultural and Biological Engineering Elevation Matlab Watershed

Monitored soil moisture and temperature in Indiana crop rotated field

10.4231/FM0P-AJ03

Keith A Cherkauer ORCID logo , Laura C Bowling ORCID logo , Stuart D Smith

11/23/2020

Soil moisture and temperature data were collected at five different depths from October 2017 to September 2020 in a crop rotated field.

Agricultural and Biological Engineering Agriculture Environment Hydrology Indiana Soil Moisture Soil Temperature Time Series Climate Data

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