ApiSense: A Weather-Aware Multimodal System to Detect Environmental Pesticide Contamination via Honeybees as Biosensors

Overview

Pesticide contamination poses a major threat to the ecosystem, costing $10-15 billion in environmental and health costs. Traditional monitoring methods such as soil and water sampling cannot effectively cover a large area or provide early warnings of contamination. However, honeybees forage over 7 km2 daily and are sensitive to extremely low concentrations of pesticides. While researchers have shown that pesticide exposure changes bee foraging behavior, few studies use foraging behavior to detect pesticide exposure. Acoustic-based approaches use shallow classifiers that cannot capture how acoustic signals change over time, while most studies ignore the impacts of weather on colony behavior. I present ApiSense: a monitoring system that uses bees as biosensors to detect pesticide contamination. ApiSense uses a gating mechanism to separate toxicological responses from those due to weather, transformers to capture long-term temporal dependencies, and a bidirectional cross-attention module to fuse foraging and acoustic signals. I applied the pesticide bifenthrin via outside feeders to 16 beehives over 15 days and collected 20,160 samples. ApiSense achieves 80.2% accuracy within 3 days of exposure, and 79.6% mean accuracy, outperforming all baseline models by at least 8%. Acoustic stability and navigation precision were identified as early indicators of pesticide exposure, aligning with toxicological findings that pesticides impair motor control and spatial memory before colony decline. ApiSense can benefit beekeepers, farmers, and regulatory agencies. At just $120 per hive, ApiSense can be deployed across the existing 2.7 million U.S. beehives to build a pesticide monitoring network that cannot be achievable by traditional methods.

Competition history

• CSEF 2026 Zoology (Senior Division) · Entry S-20-08

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