Research library

Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths

S EMI -S UPERVISED AUDIO R EPRESENTATION L EARN ING FOR M ODELING B EEHIVE S TRENGTHS Tony Zhang1, 2 , Szymon Zmyslony1 , Sergei Nozdrenkov3 , Matthew Smith1, 4 , Brandon Hopkins5 X, the Moonshot Factory, 2 Caltech, 3 Google, 4 University of Wisconsin-Madison, 5 Washington State University 1 arXiv:2105.10536v1 [cs.SD] 21 May 2021 A BSTRACT Honey bees are critical to our ecosystem and food security as a pollinator, contributing 35% of our global agriculture yield (Klein et al., 2007). In spite of their importance, beekeeping is exclusively dependent on human labor and experiencederived heuristics, while requiring frequent human checkups to ensure the colony is healthy, which can disrupt the colony. Increasingly, pollinator populations are declining due to threats from climate change, pests, environmental toxicity, making their management even more critical than ever before in order to ensure sustained global food security. To start addressing this pressing challenge, we developed an integrated hardware sensing system for beehive monitoring through audio and environment measurements, and a hierarchical semi-supervised deep learning model, composed of an audio modeling module and a predictor, to model the strength of beehives.

Download PDF

Publication details

Organizations
🇺🇸 X, the Moonshot Factory🇺🇸 Caltech🇺🇸 Google🇺🇸 University of Wisconsin-Madison🇺🇸 Washington State University
Year
2021
Type
Preprint

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs colony-health diagnostics and Varroa/queen-state alerting, sensor hardware, telemetry pipelines, and monitoring dashboards, audio-acoustic monitoring models for remote hive status detection. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.