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Approximation of functions determining colony activity using neural networks. Master thesis

Bees as a primary pollinator are an indispensable contribution to global agriculture and food production. However, their numbers have been constantly declining in recent times, primarily due to climate change, parasites or the effect of pesticide use. Understanding their behavior and reliably determine their activity and health could significantly prevent or slow down their decline. That is why this work deals with the development of a device for the acquisition of useful data from beehives, which could be used to determine the activity and health of the bees. Furthermore, this work deals with analysis of the accumulated data using machine learning methods with an emphasis on determining the activity and health of the.

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Publication details

Authors
Jakub Nevláčil
Organizations
🇨🇿 Brno University of Technology
Year
2023
Type
Thesis

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs entrance and behavior analytics in the Gratheon web app, camera-based hive-scanner and computer-vision models. 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.