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. 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 isof 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 ascondition 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 commonmetal-oxide gas sensors (in particular, the Sensirion SGP30 and the Bosch Sensortec BME680) was deployed for a substantial part of the 2020bee 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.

journal of sensors and sensor systems

An in-hive soft sensor based on phase space features for &amp;lt;i&amp;gt;Varroa&amp;lt;/i&amp;gt; infestation level estimation and treatment need detection