Журнал компьютерных наук и системной биологии

Machine Learning Programs Predict Saguaro Cactus Death

Abstract

Evans LS and Johnson CR

Objective: Determine if machine learning programs coupled with standard statistical methods can accurately predict rates of bark coverage and death of saguaro cactus plants.

Methods: Data of twelve surfaces of 1,149 saguaro cacti with four samplings over 23 years that provided more than 55,000 data points were analyzed to predict rates of bark coverage on cactus surfaces and cactus death with three machine learning programs, Validate Model, WEKA 3.8 decision trees, and Random Forest.

Results: Saguaro cacti (Carnegiea gigantea) show extensive bark coverage and cacti with extensive bark coverage die prematurely. Over the 23-year period of study, bark coverage on all surfaces was relatively constant. Decision trees are able to predict cactus death up to 96%. Three machine learning programs used similar surface coverages to make similar predictions of future bark coverage and cactus death accurately (approximately 92%), for cacti that had overall bark coverage less than 80% on south-facing surfaces. Higher prediction accuracies were obtained for cacti with were low bark percentages. While bark coverage rates and cactus death were less accurate for cacti with higher bark percentages because cacti can remain with high bark percentages with many years prior to death. Cacti with more than 80% coverage on south-facing surfaces were accurately predicted (p<0.05) to be alive and dead of the 23-year period with a tracking method.

Conclusions: The combined machine learning programs coupled with standard statistical procedures accurately predicted bark coverages and cactus death with greater than 95%.