Simulating the architecture of a termite incipient nest using a convolutional neural network
Authored by Sang-Hee Lee, Jeong-Kweon Seo, Seongbok Baik
Date Published: 2018
DOI: 10.1016/j.ecoinf.2018.02.003
Sponsors:
Korean National Research Council
Platforms:
MATLAB
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
Subterranean termites form colonies containing thousands of individuals,
and maintain these colonies by consuming wood and other materials
containing cellulose. In this consumption process, they cause serious
damage to wooden structures. Information on the population size of
termites is an important factor in developing strategies aimed at
controlling termites. In this study, we provide a reasonable possibility
of estimating the population of an incipient nest dug by a colony that
has not yet discovered any food source. We build an agent-based model to
simulate termite tunnel patterns in which the behavior of simulated
termites (agents) is governed by simple rules based on empirical data.
The simulated termites do not communicate with each other using
pheromones. They move towards the ends of tunnels, excavate when their
progress in that direction is blocked, and transport the excavated soil.
Using simulations, we determine termite tunnel patterns according to
three parameters: the number of simulated termites (N), the passing
probability of two encountering termites (P), and the distance moved by
termites to deposit soil parcels during tunneling activity (D). We train
a convolutional neural network (CNN) using 80\% of the tunnel patterns
and apply the CNN to the remaining patterns to estimate the value of N.
The application results show that the validation accuracy is
approximately 41\% and the training accuracy of the CNN is approximately
51\%. Although the validation accuracy is not high, the estimation
failures occur near the correct N values.
Tags
Agent-based model
Reticulitermes
mark-release-recapture
Population-size
Formosan subterranean termite
Different curvatures
Termite tunnel pattern
Convolutional neural network
Population
estimation
Isoptera
rhinotermitidae
Coptotermes-formosanus
Foraging
populations
Species isoptera
Tunnels