MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees

Abstract

Abstract We present a one-year-long multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were collected non-stop from April 2020 to April 2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to combine a wide variety of phenotypic trait measurements annotated by apicultural science experts with multi-sensor data, which facilitate a broader scope of analysis. We first summarize the data collection procedure, sensor data pre-processing steps, and data composition. We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns. Lastly, we showcase several hive monitoring applications based on sensor data analysis and machine learning, such as winter mortality prediction, hive population estimation, and the presence of an active and laying queen.