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 and frequently lead to hive collapse. The varroa destructor mite is a key threat to bee keeping and the monitoring of hive infestation level is of major concern for effective treatment. Sensors and automation, e.g., as in condition-monitoring and Industry 4.0, with machine learning offer help. In numerous activities a rich variety of sensors have been applied to apiary/hive instrumentation and bee monitoring. Quite recent activities try to extract estimates of varroa infestation level by hive air analysis based on gas sensing and gas sensor systems. In our work in the IndusBee4.0 project [8, 11], an hive-integrated, compact autonomous gas sensing system for varroa infestation level estimation based on low-cost highly integrated gas sensors was conceived and applied. This paper adds to 11 with the first results of a mid-term duration investigation from July to September 2020 until formic acid treatment. For the regarded hive more than 79 % of detection probability based on the SGP30 gas sensor readings have been achieved.
Keywords: Multi-modal bee health monitoring, Varroa infestation level estimation, Gas sensing, Machine learning, Apiary intelligence.
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