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Estimation of termite population size using convolution neural network
In this study we present a new approach to estimating termite populations size. So far, termite researchers have been using the mark-capture-recapture method. This method has a disadvantage that measurement time is long and error range is large. To this end, we built an agent-based model to simulate termite tunneling behavior. Using this model, we made simulated tunnel patterns that are determined by three variables: the number of simulated termites (N), the passing probability of two encountering termites (P), and the distance that termites move soil parcels (D). To explore whether the N value can be estimated with a partial termite tunnel pattern, we generated four groups of tunnel patterns that are partially obscured in complete tunnel pattern image: (1) A pattern group in which the outer area of the tunnel pattern is obscured (I-pattern), (2) a pattern group in which half of the tunnel pattern is obscured (H-pattern), (3) a pattern group in which the inner region of the tunnel pattern is obscured (O-pattern), and (4) a pattern group combining I- and O-pattern (IO-pattern). For each group, 80% of the tunnel patterns were learned through a convolution neural network (CNN) and the remaining 20% of the patterns were used for estimating N value. The estimation results showed that the N estimates for the IO-pattern are the most accurate and are in the order I-, H-, and O-patterns. This means that the termite population size can be estimated based on tunnel information near the center of the colony.
