An in-hive soft sensor based on phase space features for <i>Varroa</i> infestation level estimation and treatment need detection

Abstract

Abstract. Bees are recognized as an indispensable link in the human food chain and general ecological system. Numerous threats, from pesticides to parasites, endanger bees, enlarge the burden on hive keepers, and frequently lead to hive collapse. The Varroa destructor mite is a key threat to bee keeping, and the monitoring of hive infestation levels is of major concern for effective treatment. Continuous and unobtrusive monitoring of hive infestation levels along with other vital bee hive parameters is coveted, although there is currently no explicit sensor for this task. This problem is strikingly similar to issues such as condition monitoring or Industry 4.0 tasks, and sensors and machine learning bear the promise of viable solutions (e.g., creating a soft sensor for the task). In the context of our IndusBee4.0 project, following a bottom-up approach, a modular in-hive gas sensing system, denoted as BeE-Nose, based on common metal-oxide gas sensors (in particular, the Sensirion SGP30 and the Bosch Sensortec BME680) was deployed for a substantial part of the 2020 bee season in a single colony for a single measurement campaign. The ground truth of the Varroa population size was determined by repeated conventional method application. This paper is focused on application-specific invariant feature computation for daily hive activity characterization. The results of both gas sensors for Varroa infestation level estimation (VILE) and automated treatment need detection (ATND), as a thresholded or two-class interpretation of VILE, in the order of up to 95 % are presented. Future work strives to employ a richer sensor palette and evaluation approaches for several hives over a bee season.

A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring

“…It does not have the ability to single out the source of VOCs. It has been supported that there is a link between VOCs and Varoa destructor [ 33 ]. However, TVOC measurements cannot realistically be expected to give a direct indication of the present Varroa population, and the observed correlation or dependence can be attested as an indirect indication of changes in the behavior of the beehive due to the imposed stress of the growing mite population in the bee colony.…”

Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It

“…The precision (70%) and sensitivity (90%) of the Varroa detection rate were high, as well as the honeybee identification (100% and 70%), which means that this method is also useable to prevent a strong infection of the colonies. Some other multi-sensor bee health monitoring systems have also been developed, such as IndusBee4.0 [ 102 ] and the BeePi system [ 103 ].…”