Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives

Honeybees play a vital role in preservation of an healthy environment. Bees not only provide pollination services but also produce honey, beeswax, and royal jelly. Beekeeping has a rich history and substantial economic potential worldwide, but swarming remains a crucial challenge for maintaining profitability. Swarming, a typical colony reproductive process in honeybees, significantly impacts beekeepers profitability by lowering the number of bees in hives and thus effecting honey production. Monitoring of these beehives is therefore of paramount importance to keep an eye on their irregular behavior. Swarm prediction can be done by visually inspecting hives, monitoring temperature, or analyzing acoustic features with machine learning. Acoustic monitoring is instrumental in detecting changes in colony behavior since it overcomes the constraints of visual inspections and is not affected by external factors like temperature. In this paper, we aim to evaluate various state-of-the-art machine learning and deep learning models for swarm prediction by studying wave plot features, Mel Spectrogram, and Melfrequency Cepstral coefficients (MFCC). We use Naive Bayes, K-nearest Neighbors (KNN), and Support Vector Machines (SVM) as machine learning models and Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), and Transformers as deep learning models for comparison purposes. We apply these models on a well-known honey bees audio dataset provided by the NU-hive project and consider classification metrics such as accuracy, precision, recall, and F1 score for the comparative evaluation of our models. Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. On the other hand, CNN outperformed all the models and achieved an accuracy of 99%, using MFCC features as input data. As a result of these encouraging outcomes, we understand that our results can help the researchers to choose which AI model is more suitable for them to design beehive monitoring systems for accurate identification of abnormal situations in beehives.