As an essential and indispensable contributor to pollinating the world's crops and plants, the honey bee is key to the sustainability of humans' and our ecosystems' continued survival. Following in the footsteps of the companies TietoEvry and Beelabs project, this report also works towards monitoring bees during their daily activities. This project aims to investigate the feasibility of using wireless, battery-driven devices inside beehives to detect the sound of bees using machine learning for edge devices. Beelab has focused on measurements in and around the beehive regarding weight, temperature, barometric pressure and humidity. Sound analysis is still in its infancy with few finished working alternatives; therefore, this project will focus on the sound attribute by implementing machine learning and classification algorithms and applying it to a prototype—the progress is thoroughly documented in this report. The device records a snippet of sound and prepares to send it over a wireless transmission medium. By streamlining the code and optimizing the hardware, the device runs continuously for a month using a small, cheap battery.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-114345 |
| Date | January 2022 |
| Creators | Holmgren, Mattias, Holmér, Elias |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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