Introduction
Figure 1. Image of 4 different cultivar plots in Dr. Keenan Amundsen's Buffalo grass breeding project to determine green up. An up close example of what we will be looking at/working on for the rest of this lesson and an aesthetics visual of Buffalo Grass. (Keenan Amundsen, University of Nebraska-Lincoln)
As we learned in the previous Buffalo Grass Field Data: Scoring and Data Analysis lesson, (if you have not read through this lesson it is highly recommended you compete that lesson before you continue on with Editing and Graphing Data). Dr. Amundsen is breeding for a new Buffalo Grass cultivar that can withstand shading. Golf courses throughout the nation often create some obstacles or aesthetics by adding trees and bushes on to their course. As they do create beautiful scenery they can cause issues with shading for grass turf that cannot intercept enough light to perform photosynthesis adequately. To find a cultivar that has tolerance to shading, Dr Amundsen has performed a series of crosses in hopes of finding a cultivar tolerant to shading. To determine the effectiveness of the cross in the previous lesson, we collected data using visual appearance of the buffalo grass square plots, giving each plot a rating from 1-9 using the NTEP rating scale.
In this lesson, instead of collecting our own data we are going to use another method of data collection to analyze and edit data that has been collected by drones(Unmanned Aerial Vehicles or UAV). The UAV drones have taken images of the same plots from last lesson, but this time those same images will be scanned by a software program that calculates a percentage of greenness of a plot, by counting the number of green pixels from an image of the plot and dividing that number by the number of non green pixels in the image. As easy as it sounds there are also some challenges using an image based system for collecting data. A lot of times in the field we can encounter biotic factors such as weeds that could interfere with the percentage score or encounter some human error where images and data collection procedures can be thrown off. Which is why, this lesson focuses on analyzing raw data and determining if it is accurate or an outlying error.
Continue on to the next page to watch a more in depth video on this form of data collection and how to properly analyze Buffalo Grass data.