Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples

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