用图神经网络分析蜂箱间疾病传播，提升蜂群病发预测精度。
STAG-CN: Spatio-Temporal Apiary Graph Convolutional Network for Disease Onset Prediction in Beehive Sensor Networks
- 构建蜂箱时空图网络，融合物理位置与气候关联信息。
- 三日预测F1达0.607，气候相似性比物理距离更具预测力。
- 适合精准养蜂与农业生物安全监测场景。
蜜蜂群落损失威胁全球授粉服务，但现有监测系统将每个蜂箱视为孤立单元，忽略了疾病在蜂场间的空间传播路径。本文提出时空蜂场图卷积网络（STAG-CN），通过图神经网络建模蜂箱间关系以预测疾病爆发。STAG-CN基于物理邻近性和气候传感器相关性构建双重邻接图，利用因果空洞卷积与切比雪夫谱图卷积组成的时-空-时结构处理多变量物联网传感器数据。在韩国人工智能枢纽养蜂数据集（数据集#71488）上，采用扩展窗口时间交叉验证，其在三日预测时达到F1分数0.607。消融实验表明，仅使用气候邻接矩阵即可达到全模型性能（F1=0.607），而仅使用物理邻接矩阵的F1为0.274，说明共享环境响应模式比空间邻近性蕴含更强预测信号。结果证明了基于图的生物安全监测在精准养蜂中的可行性，表明蜂箱间传感器相关性包含单蜂箱方法无法捕捉的疾病相关信息。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。