Predicting Bark Rates of Saguaro Cacti
George
1Department
1 Kennedy ,
Dr. Ehsan
1 Atefi
and Dr. Lance
of Mechanical Engineering, Manhattan College, 2Department of Biology, Manhattan College
gkennedy01@manhattan.edu
Epidermal browning or bark coverage occurs on saguaro cacti native to Tucson, Arizona due to sunlight exposure. Using the database of cactus bark coverages, it is possible to predict epidermal bark rates in future years. The Classification Learner App in Matlab was used for its supervised machine learning to train models relevant to the database of bark coverages.
The Saguaro cacti have vertical ribs with crests (protrusion) and are separated by convex troughs. For each crest and trough, a surface eight cm long was evaluated at 1.75 meters from the ground. The percent bark coverage for each surfaces was then estimated visually. The percentages were compiled in an excel spreadsheet in a format compatible with the machine learning application.
Materials and Methods
Machine Learning Inputs
Introduction
2 Evans
Saguaro cacti were studied in Tucson Mountain Park. In 1994, 50 permanent plots with 1149 cacti were randomly selected. The selected cacti were all taller than 4 meters. Cactus morphological features of cacti, characteristics of nearby vegetation, topographical features and GPS data were used to identify cacti for each field evaluation.
Machine Learning Results
The above decision tree was an output of the classification application in Matlab. It shows the cacti being classified by the percentage of bark on their North Right Trough.
The figure above is a scatter plot of data to predict classes 1 through 5 based upon data from 1994 with an accuracy of 99.6%. The classification app generated a scatter plot for the model being trained. The predictor surfaces can also be changed in order to look at different relationships between surfaces and bark coverages.
This confusion matrix is a representation of the accuracy with which the application classifies cacti based on bark coverage.
The authors would like to show appreciation to Dr. Lance Evans for collecting and providing the data and providing the funds to conduct this study, without which, this study could not have been possible.