STAG-CN: Spatio-Temporal Apiary Graph Convolutional Network for Disease Onset Prediction in Beehive Sensor Networks

Abstract

Honey bee colony losses threaten global pollination services, yet current monitoring systems treat each hive as an isolated unit, ignoring the spatial pathways through which diseases spread across apiaries. This paper introduces the Spatio-Temporal Apiary Graph Convolutional Network (STAG-CN), a graph neural network that models inter-hive relationships for disease onset prediction. STAG-CN operates on a dual adjacency graph combining physical co-location and climatic sensor correlation among hive sessions, and processes multivariate IoT sensor streams through a temporal--spatial--temporal sandwich architecture built on causal dilated convolutions and Chebyshev spectral graph convolutions. Evaluated on the Korean AI Hub apiculture dataset (dataset \#71488) with expanding-window temporal cross-validation, STAG-CN achieves an F1 score of 0.607 at a three-day forecast horizon. An ablation study reveals that the climatic adjacency matrix alone matches full-model performance (F1\,=\,0.607), while the physical adjacency alone yields F1\,=\,0.274, indicating that shared environmental response patterns carry stronger predictive signal than spatial proximity for disease onset. These results establish a proof-of-concept for graph-based biosecurity monitoring in precision apiculture, demonstrating that inter-hive sensor correlations encode disease-relevant information invisible to single-hive approaches.

Figures (6)

• Figure 1: Schematic disease timeline for group 01_1, the only apiary group exhibiting disease onset during the overlap period. Filled circles indicate disease-positive labels; open circles indicate healthy labels. Disease appears in late August and spreads across sessions in a staggered pattern, consistent with inter-hive contagion dynamics.
• Figure 2: Dual adjacency graph construction. The physical adjacency $\mathbf{A}_{\mathrm{phys}}$ captures binary co-location within apiary groups. The climatic adjacency $\mathbf{A}_{\mathrm{clim}}$ captures pairwise Pearson correlation of sensor time series across groups. The combined matrix $\mathbf{A}$ is a weighted blend controlled by $\lambda$, followed by self-loop addition and symmetric normalization to produce $\tilde{\mathbf{A}}$.
• Figure 3: STAG-CN architecture. The input tensor passes through a linear projection, two stacked ST-Blocks (each containing a TCN--GCN--TCN sandwich with residual connections and layer normalization), temporal pooling at the last time step, and a two-layer classifier producing per-node disease probabilities. The normalized adjacency $\tilde{\mathbf{A}}$ is shared across all spatial convolution layers.
• Figure 4: Expanding-window temporal cross-validation with three folds. Each fold trains on all data preceding the test window, ensuring that the model never observes future samples during training. The training set grows with each fold while the test window remains fixed in size.
• Figure 5: ROC curve for LOGO CV (group 01_1 held out). The high AUC confirms strong discriminative ability despite poor F1 at the default threshold.