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

TL;DR

This paper introduces MSPB, a comprehensive longitudinal multi-sensor dataset from honey bee colonies, combining sensor data and phenotypic traits to enable advanced analysis and machine learning applications in apiculture.

Contribution

The study provides one of the first extensive datasets with expert-annotated phenotypic traits and sensor data, supporting diverse honey bee research and monitoring applications.

Findings

Dataset includes audio, temperature, humidity, and phenotypic data.

Demonstrated applications include mortality prediction and hive health assessment.

Data visualization and analysis facilitate future research in bee colony monitoring.

Abstract

We present a longitudinal multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were continuously collected between May-2020 and April-2021 from 53 hives located at two apiaries in Qu\'ebec, 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 provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, 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.