Institute of

Cognitive Integrated Sensor Systems

Prof. Dr.-Ing. Andreas König







Student Theses




IndusBee 4.0 - Sensory Systems and Machine Learning for Optimized Bee-Tending and Hive Keepers Assistance



Due to remarkable work like the 'More than Honey' documentary, it has entered public conciousness, that bees play a crucial role in our ecosystem, but are subject to major detrimental influences and threats from pesticides to parasites. This imposes an increasingly hard to meet load and responsibility on hive keepers. Numerous activities have been triggered in the last decade, trying to improve bee monitoring and hive keeping by tools and techniques from related disciplines, e.g., applying sensors and monitoring procedures from general automation activities, home automation in particular. The work presented here, targets on bringing knowledge and infrastructure from the recent field of Industry 4.0 in conjunction with integration technologies for sensors and electronics with embedded Machine Learning and Optimization techniques to exploit automation concepts for bee keeping, to achieve efficient hive keeper assistance systems, and last not least to minimize the required hive opening and intrusion  by  sensor  based monitoring. The investigated techniques will be validated and extended in the bee hives of my BeeLab.


I was lucky, that from early childhood until the end of my engineering education, bee keeping was always present in my life and triggered my curiosity on bees sensory capabilities and inherent swarm intelligence . The contact with bees provided me with improved understanding of nature and dependencies in our ecosystem, though it also gave to me in my boyhood the painful experience of monocular vision for nearly a week. The tending of bee colonies throughout the seasons of the year was, and in part still is very much based on the individual expertise, observation capability, and  the right decisions of hive keepers. The increasing detrimental influences by environmental challenges and threats from pesticides to parasites led to a dramatic increase in bee colony mortality and impose an increasing load on hive-keepers. As an engineer, the immediate question was and still is, whether tasks could be automated and improved, reducing the manual load of regular check-ups with the related opening of the hives, as well as the expertise required by resorting to resources and techniques from sensing, measurement systems, and from Machine Learning to continously monitor and automatically assess bee colony condition, and further support the hive-keeper by the introduction of appropriate assistance systems. The fields of Ambient Intelligence, (Home) Automation, IoT, and Industry 4.0 provide the leverage by infrastructure and methods to be adopted to this ecologically crucial domain. One subproblem in this field is the analysis of honey quality, e.g., from food fraudery activities related to honey various sources of contamination, blending etc. to on-site quality assessment. This started to be addressed and is still pursued in one our related projects an Lab-on-Spoon/Fork in the context of our E-Taster system as mobile food inspection devices, also for honey quality:


Numerous engineers and hive keepers have been attracted to that field of activity in the last decade and hive instrumentation has seen significant attention and advance. Most commonly, hive temperature and moisture is measured, logged, and communicated to hive keepers mobile or related devices. Hive weight monitoring has also been widespread and commercial solutions are available. Bee activity has been deduced from weight change over the day, but also by extra sensors, and additionally, the feeder level module monitoring has been added. More recent approaches from about 2012 onward until today extend the employed sensory palette to video and audio sensors, e.g., cameras and microphones. In addition to instrumentation and communication, intelligent monitoring and decision making massively has entered the field. Examples of these activities can be found, e.g., under hive-monitoring (with a focus on cheap Arduino based solutions, Bee-Counter etc.), IoBee, BeeandMe, or last not least the OSBH project. There are an increasing number of activities, publications, and also patents. However, there is still a little headroom for extensions and improvements related to adding new sensory capability, advanced information processing capability, and employ advanced packaging technology, e.g., from SiP (System-in-Package) domain for both increased robustness as well as feasible prices of the required hive instrumentation. Here ISE experiences, from sensor and electronics integration technology to automated intelligent system design, in particular, from our Industry 4.0 related activities, e.g., in the BMBF-project MoSeS-Pro  or in Polymer Film Industry process optimization, are going to be exploited. Currently, based on low-cost Arduino & Raspberry PI platforms, a modular hive instrumentation and intelligent monitoring system is evolved, that comprises the following features under cost and integration constraints:
On example of alleviating hive keepers task is related to the required routine inspection for  the degree of Varoa mite investation of the colony. In the  currently dominant modular magazine hives with open bottom, the colony debris including dead mites drops onto a collecting 'dipher', that is manually inspected for each hive for parasites, counting the number as an assessment figure of the hives condition and treatment requirements. Common methods from Machine Vision and automated quality control can be adopted to automate and accelerate this process:


The various pieces of debris, e.g., wax, propolis, bee limbs, and varoa mites can be processed by image segmentation approach detecting candidate regions for further classification (blobs shown in right picture). Then the regions representing varoa mites can be automatically identified in the next step, and the mite regions can be counted. This assistance promises to provide substantial speed-up for owners of large hive numbers and additionally allows a detailed archiving of each colony's results. Currently, a dedicated scanner setup is pursued and a more simple mobile phone App based variant is considered.

For further visual remote hive monitoring, low-cost cameras for close-up view with active illumination outside the bees perceptive range (> 530 nm), and even thermographic monitoring with moderate resolution can be considered.In addition to remote interactive monitoring, Machine Vision and Learning techniques for automated continuous monitoring and focused alert generation for hive keepers can be considered:


In the next steps of the work on IndusBee 4.0 the following objectives wil be pursued:

The project is entirely funded by private funds from Prof. Dr.-Ing. Andreas König                         

  Status:   Running, duration:  2017 - retirement and beyond
  Partners:   -
  Financing:   Self-funded research
  PI/Contact:   Prof. Dr.-Ing. Andreas König
  Contributors:   Prof. Dr.-Ing. Andreas König

Andreas König. IndusBee 4.0 – Sensorik und maschinelles Lernen für optimierte Bienenhaltung und Imkerassistenz. Eingeladener Vortrag im Rahmen der Hans Eisenmann-Akademie (Abstract, YouTube), 16. Mai,  Hans Eisenmann-Forum für Agrarwissenschaften, TU München, 2019
      A. König, Extending Bee Hive Health State Monitoring by Integrated Acoustical Sensing and Machine Learning, in Proc. 5th Int. Conf. on Sensors and Electronic Instrumentation Advances (SEIA 2019), Canary Islands (Tenerife), Spain, Sept. 25-27, 2019.
      A. König, VarroaCounter - Towards Automating the Varroa Screening for Alleviated Bee Hive Treatment, in Proc. 5th Int. Conf. on Sensors and Electronic Instrumentation Advances (SEIA 2019), Canary Islands (Tenerife), Spain, Sept. 25-27, 2019.  
      Andreas König, IndusBee4.0 -- Integrated Intelligent Sensory Systems for Advanced Bee Hive Instrumentation and Hive Keeper's Assistance Systems, Sensors & Transducers Journal, 237.9-10, pp. 109-121, 2019
      A. König. Integrated Intelligent Sensor Systems for In-Hive Varroa Infestation Control in Digital Bee Keeping. in Proc. of SMSI 2020, Vol. Sensors and Instrumentation, Chapter B3 Biosensors and Sensors for Biology, pp. 111--112, 2020. DOI 10.5162/SMSI2020/B3.1  
      A. König, BeE-Nose - An In-Hive Multi-Gas-Sensor Extension to the IndusBee4.0 System for Hive Air Quality Monitoring and Varroa Infestation Level Estimation, in "Advances in Signal Processing: Reviews", Book Series (S. Y. Yurish, Ed.), IFSA Publishing, Vol. 2, 2021, pp. 443-463.  
      Andreas König, First Results of the BeE-Nose on Mid-Term Duration Hive Air Monitoring for Varroa Infestation Level Estimation, Sensors & Transducers Journal, 250.3, March, pp. 39-43 , 2021
      A. König, Cognitive Integrated Sensor Systems for In-Hive Varroa Infestation Level Estimation based on Temperature-Modulated Gas Sensing, to be published in Sensor and Measurement SMSI Science International (SMSI), 2021.