Author ORCID Identifier
Date Available
6-15-2023
Year of Publication
2022
Degree Name
Master of Science (MS)
Document Type
Master's Thesis
College
Agriculture, Food and Environment
Department/School/Program
Plant and Soil Sciences
First Advisor
Dr. Christopher D. Teutsch
Abstract
The following studies investigate the accuracy and practicality of exploiting the color dichotomy present between C3 and C4 grass species to estimate their respective proportions from drone or camera captured imagery. Understanding the proportions of C3 and C4 grasses in pastures is vital to sound decision making for livestock production. The ability to monitor these proportions remotely will also allow for large scale monitoring as well as detection of changes in botanical composition over time and in response to weather events, management, or climate change. A free green canopy cover (GCC) analyzing software, Canopeo, was used to quantify green plants in captured images, providing an estimation of C3 grasses that retain green color in colder seasons while C4 grasses do not. The GCC estimates from Canopeo were compared to either a known established species proportion, or what was measured using occupancy grids. We found that green canopy cover software could estimate the proportion of C3 grasses in images captured by a drone and a Nikon camera with reasonable accuracy.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2022.413
Funding Information
This project was funded by a Non-Assistance Cooperative Agreement with the United States Department of Agriculture Agricultural Research Service Food Animal Production Unit, Lexington, KY (2/06/19 215 Dinkins 5042-21000-003-00D)
Recommended Citation
Bush, Jordyn Alyssa, "Remote Sensing for Quantifying C3 and C4 Grass Ratios in Pastures" (2022). Theses and Dissertations--Plant and Soil Sciences. 162.
https://uknowledge.uky.edu/pss_etds/162
