The study of honey bee health has received special attention in the last years. Researchers has been monitoring physical variables to determinate the status of the colony. This is a first approach in the development of a real time monitoring system to provide useful information to beekeepers that will help them to prevent colony losses. This study presents an analysis of the sound from two colonies of bees in the Mel frequency domain. The first is a healthy colony with queen and the second one is a hive with no queen and with a reduced population. Sound samples were acquired for each colony and characterized using Mel Frequency Cepstral Coefficients (MFCC). To summarizes the information, statistical descriptors was obtained for each Mel coefficient. An exploratory analysis of samples revealed two different hive characteristics; the presence and lack of a queen bee. For honey bee buzz recognition, a Logistic Regression Model was used. The preliminary results show that it is possible to classify both characteristics obtaining high classification rates using a reduced set of features.

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In precision beekeeping, the automatic recognition of colony states to assess the health status of bee colonies with dedicated hardware is an important challenge for researchers, and the use of machine learning (ML) models to predict acoustic patterns has increased attention. In this work, five classification ML algorithms were compared to find a model with the best performance and the lowest computational cost for identifying colony states by analyzing acoustic patterns. Several metrics were computed to evaluate the performance of the models, and the code execution time was measured (in the training and testing process) as a CPU usage measure. Furthermore, a simple and efficient methodology for dataset prepossessing is presented; this allows the possibility to train and test the models in very short times on limited resources hardware, such as the Raspberry Pi computer, moreover, achieving a high classification performance (above 95%) in all the ML models. The aim is to reduce power consumption and improves the battery life on a monitor system for automatic recognition of bee colony states.

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