Estimating termite population size using spatial statistics for termite tunnel patterns

•We generate a simulated termite tunnel pattern using an agent-based model.•We use k-NN to learn the relationship between termite tunnel patterns and fractal dimensions.•We estimate termite population size using the learned k-NN. Subterranean termites build underground tunnels for foraging. The obtained food is transported to the nest through these tunnels, and consumed to maintain the termite colony. In this process, termites can cause damage to wooden structures. To develop effective control strategies to reduce termite damage, it is important to know the sizes of the termite populations in the tunnels. In this study, we proposed a method for estimating the termite population size using the spatial statistic indices including fractal dimension (FD), local density (LD), and join count statistic (JCS) for the tunnel patterns. However, the method needs further improvement to be applied in field conditions. For the method, we generated 8,000 tunnel pattern images (1,000 images for each N) using an agent-based model based on experimental data. Here, N (= 3, 4, ..., 10) represents the number of termites participating in tunnel construction in the simulation. Subsequently, we calculated the FD, LD and JCS values of the tunnel pattern and trained and verified the k-nearest neighbors (KNN) algorithm, using 5,600 and 2,400 images, respectively. The population size (N) was estimated based on the FD, LD and JCS using the KNN algorithm. The estimated accuracy for all N was 60% to 97% in the range of k = 1 to 300. If the model for tunnel pattern generation includes heterogeneous environmental conditions, the proposed method could be used to effectively estimate the actual number of termite populations. Finally, we briefly discuss the challenges affecting our model, and how these could be overcome.