Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation. Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant patterns and information have been extracted by processing the video/image data, but no work has been done using audio data. This research inaugurates and takes the first step towards the use of audio data in the Electronic Beehive Monitoring System (BeePi) by enabling a path towards the automatic classification of audio samples in different classes and categories within it. The experimental results give an initial support to the claim that monitoring of bee buzzing signals from the hive is feasible, it can be a good indicator to estimate hive health and can help to differentiate normal behavior against any deviation for honeybees.

The beekeepers need to inspect their hives regularly in order to protect them from various stressors. Manual inspection of hives require a lot of time and effort. Hence, many researchers have started using electronic beehive monitoring (EBM) systems to collect critical information from beehives, so as to alert the beekeepers of possible threats to the hive. EBM collects information by applying multiple sensors into the hive. The sensors collect information in the form of video, audio or temperature data from the hives. This thesis involves the automatic classification of audio samples from a beehive into bee buzzing, cricket chirping and ambient noise, using machine learning models. The classification of samples in these three categories will help the beekeepers to determine the health of beehives by analyzing the sound patterns in a typical audio sample from beehive. Abnormalities in the classification pattern over a period of time can notify the beekeepers about potential risk to the hives such as attack by foreign bodies (Varroa mites or wing virus), climate changes and other stressors.

This thesis describes the power analysis of the electronic beehive monitoring system. The electronic beehive monitoring system was made to work either with a UB12120 12V 12Ah standard lead-acid battery or an Anker (TM) Astro E7 5V lithium-ion battery to analyze the power requirements. These batteries are recharged by Renogy 50Watt 12 Volt Monocrystalline Solar Panel. Power analysis is performed using both batteries to calculate system’s efficiency. The performed power analysis indicates that the Anker (TM) Astro E7 26800mAh 5V lithium-ion battery runs approximately 6 hours more than the lead acid battery. Moreover, the lithium-ion battery is compact, has a lighter weight, is more efficient, and has a longer cycle life. Using lithium-ion batteries will likely result in fewer hardware components and a smaller environmental footprint.