Login
Toggle navigation
Home
ICPA
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Leadership
ISPA Leadership
Officers
Past Presidents
Officer Responsibilities
Country Representatives
Communities
Community Guidance
On-Farm Experimentation
Nitrogen
Latin America
Economics
African Association for Precision Agriculture
Membership
ISPA Member Benefits
Membership Form
Events
ISPA Events
ACPA
ACPA Proceedings
AfCPA
AfCPA Proceedings
CLAP
CLAP Proceedings
ECPA
ECPA Proceedings
ICPA
ISPA Webinars
OFE
AAPA
Latin American
Robotics and Automation Symposium
Event Overview
Registration
Program
Venue
Speakers
About ISPA
Newsletters
History
Jobs
Precision Ag Definition
Agriculture Course Database Submission
Publications
ICPA Proceedings
ECPA Proceedings
Contact Us
Members
Suggestion Form
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Proceedings
Search
Authors
Topics
Years
Types
Find matching any:
Reset
» Add more topics
Add filter to result:
Estimation of Vegetative Biomass Using On-the-Go Mobile Sensors
J. Pittman
Noble Foundation/ Oklahoma State Univ.
Non-destructive methods for estimation of vegetative biomass have been developed using several remote sensing strategies as well as physical measurement techniques. An effective method for estimating biomass must be at least as accurate as the accepted standard for destructive removal measurement techniques such as a forage harvester or quad harvest strategies. In large part vegetative biomass is considered a function of canopy or plant height. Subsequently, a method or piece of equipment which can estimate a height component is typically implemented for collecting measurements and from those measurements a relationship is created between height and mass. A number of sensing technologies have been examined for such applications. This study examined several types of ground-based sensing strategies for use in estimation of in-field forage biomass. A forage production trial consisting of multiple fertilizer treatments and mixed as well as mono culture species treatments was employed as an evaluation platform for the performance of the sensor estimation as compared to physical removal harvests. Predictive models were constructed and comparisons of sensor based estimates made to physically measured biomass harvested by hand from quad harvests as well as machine harvests using a forage harvester. Statistical analysis for both methods of harvest and sensor estimated harvests were performed as would normally be done according to treatment structure. Mean estimates were examined for evaluation of differences between biomass evaluation methods for each treatment. This strategy was employed in order to evaluate the difference if any on the overall research implications for data which was generated from physical collection techniques as well as sensor estimated data. Ultimately differences were minimal and did not contribute to disparity in implications for research aspects of the trial. Additionally, statistical analysis was performed on a subset of the data for repeatability. Paired identical plots were compared using Limits of agreement analysis to evaluate the repeatability of each technique. This analysis produced more narrow error bands for the sensing data as compared to the harvested data which suggests the sensing data is at least as stable as the physically harvested data. Subsequently, using ground-based mobile sensing for data collection which are incorporated into a biomass estimation model could prove to be effective in rapid accurate in field biomass estimation.
Keyword
: non destructive biomass estimation
J. Pittman
Remote Sensing Applications in Precision Agriculture
Oral
2014
Download paper