Articles | Volume 11, issue 1
https://doi.org/10.5194/jsss-11-29-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/jsss-11-29-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An in-hive soft sensor based on phase space features for Varroa infestation level estimation and treatment need detection
Andreas König
CORRESPONDING AUTHOR
Institute of Cognitive Integrated Sensor Systems, Technische Universität Kaiserslautern, Erwin-Schrödinger-Straße 12, 67663 Kaiserslautern, Germany
Related authors
Hamam Abd and Andreas König
J. Sens. Sens. Syst., 11, 233–262, https://doi.org/10.5194/jsss-11-233-2022, https://doi.org/10.5194/jsss-11-233-2022, 2022
Short summary
Short summary
We pursue a promising novel self-adaptive spiking neural analog-to-digital data conversion (SN-ADC) design that uses spike time to carry information. Thus, SN-ADC can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things and Industry 4.0 and is based on a self-adaptive CMOS memristor.
Qummar Zaman, Senan Alraho, and Andreas König
J. Sens. Sens. Syst., 10, 193–206, https://doi.org/10.5194/jsss-10-193-2021, https://doi.org/10.5194/jsss-10-193-2021, 2021
Short summary
Short summary
A novel experience replay particle swarm optimization algorithm is presented and successfully deployed to improve the optimization performance for reconfigurable analog integrated circuits of industry 4.0. An optimization approach is introduced that relied on THD-based indirect measurement method that varies from the traditional calibration approach. Instead, the proposed calibration methodology optimizes all characteristics of the reconfigurable amplifier at once.
A. König and K. Thongpull
J. Sens. Sens. Syst., 4, 63–75, https://doi.org/10.5194/jsss-4-63-2015, https://doi.org/10.5194/jsss-4-63-2015, 2015
Short summary
Short summary
A research activity in the context of ambient assisted-living and Internet of Things applications is presented. A new assistance system with an integrated, multi-sensor, low-cost, autonomous, smart spoon device, denoted as Lab-on-Spoon, has been conceived. The goal is to provide assistance for unskilled or challenged consumers by sensory context in ingredients and cooking results.
Successful liquid ingredient recognition and quality assessment, i.e., rotting and contamination, was achieved.
Hamam Abd and Andreas König
J. Sens. Sens. Syst., 11, 233–262, https://doi.org/10.5194/jsss-11-233-2022, https://doi.org/10.5194/jsss-11-233-2022, 2022
Short summary
Short summary
We pursue a promising novel self-adaptive spiking neural analog-to-digital data conversion (SN-ADC) design that uses spike time to carry information. Thus, SN-ADC can be effectively translated to aggressive new technologies to implement reliable advanced sensory electronic systems. The SN-ADC supports self-x (self-calibration, self-optimization, and self-healing) and machine learning required for the internet of things and Industry 4.0 and is based on a self-adaptive CMOS memristor.
Qummar Zaman, Senan Alraho, and Andreas König
J. Sens. Sens. Syst., 10, 193–206, https://doi.org/10.5194/jsss-10-193-2021, https://doi.org/10.5194/jsss-10-193-2021, 2021
Short summary
Short summary
A novel experience replay particle swarm optimization algorithm is presented and successfully deployed to improve the optimization performance for reconfigurable analog integrated circuits of industry 4.0. An optimization approach is introduced that relied on THD-based indirect measurement method that varies from the traditional calibration approach. Instead, the proposed calibration methodology optimizes all characteristics of the reconfigurable amplifier at once.
A. König and K. Thongpull
J. Sens. Sens. Syst., 4, 63–75, https://doi.org/10.5194/jsss-4-63-2015, https://doi.org/10.5194/jsss-4-63-2015, 2015
Short summary
Short summary
A research activity in the context of ambient assisted-living and Internet of Things applications is presented. A new assistance system with an integrated, multi-sensor, low-cost, autonomous, smart spoon device, denoted as Lab-on-Spoon, has been conceived. The goal is to provide assistance for unskilled or challenged consumers by sensory context in ingredients and cooking results.
Successful liquid ingredient recognition and quality assessment, i.e., rotting and contamination, was achieved.
Related subject area
Applications: Environmental monitoring
The River Runner: a low-cost sensor prototype for continuous dissolved greenhouse gas measurements
Laboratory robustness validation of a humidity sensor system for the condition monitoring of grease-lubricated components for railway applications
A classification technique of civil objects by artificial neural networks using estimation of entropy on synthetic aperture radar images
Measure particulate matter by yourself: data-quality monitoring in a citizen science project
An autonomous flame ionization detector for emission monitoring
Gas sensors for climate research
Metal ion binding and tolerance of bacteria cells in view of sensor applications
Highly sensitive benzene detection with metal oxide semiconductor gas sensors – an inter-laboratory comparison
Homogenous static magnetic field coils dedicated to portable nuclear magnetic resonance for agronomic studies
In situ measurements of O2 and CO eq. in cement kilns
Scanning method for indoor localization using the RSSI approach
High-temperature CO / HC gas sensors to optimize firewood combustion in low-power fireplaces
Atmospheric transmission coefficient modelling in the infrared for thermovision measurements
Partially integrated cantilever-based airborne nanoparticle detector for continuous carbon aerosol mass concentration monitoring
Luminescent determination of nitrite traces in water solutions using cellulose as sorbent
Catalytic metal-gate field effect transistors based on SiC for indoor air quality control
Characterization of ash particles with a microheater and gas-sensitive SiC field-effect transistors
Catalytic and thermal characterisations of nanosized PdPt / Al2O3 for hydrogen detection
Selective detection of hazardous VOCs for indoor air quality applications using a virtual gas sensor array
Aerosol-deposited BaFe0.7Ta0.3O3-δ for nitrogen monoxide and temperature-independent oxygen sensing
A novel horizontal to vertical spectral ratio approach in a wired structural health monitoring system
Sensing of gaseous malodors characteristic of landfills and waste treatment plants
Martin Dalvai Ragnoli and Gabriel Singer
J. Sens. Sens. Syst., 13, 41–61, https://doi.org/10.5194/jsss-13-41-2024, https://doi.org/10.5194/jsss-13-41-2024, 2024
Short summary
Short summary
Greenhouse gas emissions from freshwaters are not well quantified on a global scale. Our prototype monitors aqueous concentrations of CH4 and CO2 at relevant timescales. Our low-cost design allows the application of replicated sensors to capture spatial heterogeneity and temporal variability in dissolved gases in highly dynamic ecosystems, thereby addressing a major bottleneck in the reliable estimation of emissions. We provide a detailed prototype description and share software code.
Krisztián Dubek, Christoph Schneidhofer, Nicole Dörr, and Ulrich Schmid
J. Sens. Sens. Syst., 13, 9–23, https://doi.org/10.5194/jsss-13-9-2024, https://doi.org/10.5194/jsss-13-9-2024, 2024
Short summary
Short summary
A new method for water detection in lubricated rail components is presented. It is based on a robust humidity sensor combined with robust data evaluation to determine the water content of greases. Based on a laboratory evaluation in the relevant environment, the presented approach offers an online monitoring tool to predict the water content of grease-lubricated rail parts, thereby enhancing the reliability and safety while reducing the maintenance costs and downtime of railway wagons.
Anton V. Kvasnov and Vyacheslav P. Shkodyrev
J. Sens. Sens. Syst., 10, 127–134, https://doi.org/10.5194/jsss-10-127-2021, https://doi.org/10.5194/jsss-10-127-2021, 2021
Short summary
Short summary
We have prepared an article that demonstrates one of the ways to recognize objects on the ground surface. This paper is a result of experimental data that were collected with unnamed aerial vehicles (UAVs) with synthetic aperture radar. Although UAV radar has a small monitoring area, we noted that such pictures can contain the steady features of the mutual arrangement between detected objects. We have constructed an artificial neural network that solves the tasks of group object recognition.
Aboubakr Benabbas, Martin Geißelbrecht, Gabriel Martin Nikol, Lukas Mahr, Daniel Nähr, Simon Steuer, Gabriele Wiesemann, Thomas Müller, Daniela Nicklas, and Thomas Wieland
J. Sens. Sens. Syst., 8, 317–328, https://doi.org/10.5194/jsss-8-317-2019, https://doi.org/10.5194/jsss-8-317-2019, 2019
Jan Förster, Winfred Kuipers, Christian Lenz, Steffen Ziesche, and Franz Bechtold
J. Sens. Sens. Syst., 8, 67–73, https://doi.org/10.5194/jsss-8-67-2019, https://doi.org/10.5194/jsss-8-67-2019, 2019
Short summary
Short summary
Reliable detection of hydrocarbons can be achieved with a flame ionization detector (FID). However, these devices have not been implemented as true field devices yet. Miniaturization by using ceramic multilayer technology leads to a strong reduction of gas consumption and allows autonomous operation of the FID with gas supply by electrolysis. Thus, this research enables the use of the FID in the field. Characterization of this new FID reveals a performance comparable to conventional FIDs.
Louisa Scholz, Alvaro Ortiz Perez, Benedikt Bierer, Jürgen Wöllenstein, and Stefan Palzer
J. Sens. Sens. Syst., 7, 535–541, https://doi.org/10.5194/jsss-7-535-2018, https://doi.org/10.5194/jsss-7-535-2018, 2018
Short summary
Short summary
The availability of datasets providing information on the spatial and temporal evolution of greenhouse gas concentrations is of high relevance for the development of reliable climate simulations. Here we present a novel, non-dispersive infrared absorption spectroscopy (NDIR) device that can possibly act as a central building block of a sensor node to provide high-quality data of carbon dioxide (CO2) concentrations under field conditions at a high measurement rate.
Jonas Jung, Anja Blüher, Mathias Lakatos, and Gianaurelio Cuniberti
J. Sens. Sens. Syst., 7, 433–441, https://doi.org/10.5194/jsss-7-433-2018, https://doi.org/10.5194/jsss-7-433-2018, 2018
Short summary
Short summary
We assessed the applicability of bacterial surface layer proteins of Lysinibacillus sphaericus JG-B53 and Sporosarcina ureae ATCC 13881 for the detection of metal ions in water. Based on the interactions of the cell components with metal complexes, two potential sensor systems, one colorimetric with functionalized gold nanoparticles and the other using a regenerative sensor layer, were developed. The systems' detection limits of YCl3 in water were 1.67 x 10−5 and 1 x 10−4 mol L−1, respectively.
Tilman Sauerwald, Tobias Baur, Martin Leidinger, Wolfhard Reimringer, Laurent Spinelle, Michel Gerboles, Gertjan Kok, and Andreas Schütze
J. Sens. Sens. Syst., 7, 235–243, https://doi.org/10.5194/jsss-7-235-2018, https://doi.org/10.5194/jsss-7-235-2018, 2018
Short summary
Short summary
For detection of benzene, a multichannel gas sensor system was tested in two different laboratories at the concentration range from 0.5 ppb up to 10 ppb. A model is used to extract the channels and multilinear regression is done to compensate cross interference to other gases. Depending on the measurement conditions, the quantification accuracy is between ±0.2 ppb and ±2 ppb. Regression models for one laboratory were transferable between the labs under comparable measurement conditions.
Rahima Sidi-Boulenouar, Ariston Reis, Eric Nativel, Simon Buy, Pauline de Pellegars, Pan Liu, Michel Zanca, Christophe Goze-Bac, Jérome Barbat, Eric Alibert, Jean-Luc Verdeil, Frédéric Gatineau, Nadia Bertin, Atma Anand, and Christophe Coillot
J. Sens. Sens. Syst., 7, 227–234, https://doi.org/10.5194/jsss-7-227-2018, https://doi.org/10.5194/jsss-7-227-2018, 2018
Olga Driesner, Fred Gumprecht, and Ulrich Guth
J. Sens. Sens. Syst., 6, 327–330, https://doi.org/10.5194/jsss-6-327-2017, https://doi.org/10.5194/jsss-6-327-2017, 2017
Ahmad Warda, Bojana Petković, and Hannes Toepfer
J. Sens. Sens. Syst., 6, 247–251, https://doi.org/10.5194/jsss-6-247-2017, https://doi.org/10.5194/jsss-6-247-2017, 2017
Short summary
Short summary
We studied the problem of wireless indoor mobile robot localization and tracking using noise-free data and data with additive white Gaussian noise at three receiver positions. We proposed a new scanning method to overcome the drawbacks of fingerprint, which includes time-consuming construction of a database and its need for rebuilding every time a significant change in the environment occurs.
Binayak Ojha, Navas Illyaskutty, Jens Knoblauch, Muthu Raman Balachandran, and Heinz Kohler
J. Sens. Sens. Syst., 6, 237–246, https://doi.org/10.5194/jsss-6-237-2017, https://doi.org/10.5194/jsss-6-237-2017, 2017
Short summary
Short summary
A novel combustion airstream control concept has been developed based on in situ sensors for combustion temperature, residual oxygen concentration and residual un-combusted CO / HC components. The implementation of this control concept allows for a large reduction in toxic gas emissions by up to 80 % compared to hand-operated furnaces. A stable long-term CO / HC sensor for such an application is not available; thus, the long-term sensor signal stability of different CO / HC sensors is studied.
W. Minkina and D. Klecha
J. Sens. Sens. Syst., 5, 17–23, https://doi.org/10.5194/jsss-5-17-2016, https://doi.org/10.5194/jsss-5-17-2016, 2016
Short summary
Short summary
The aim of this paper is to discuss different models that describe atmospheric transmission in the infrared. They were compared in order to choose the most appropriate one for certain atmospheric conditions. Universal models and different inaccuracies connected with them were analysed in this paper. There have been models analysed from the literature, and these are used in infrared cameras.
H. S. Wasisto, S. Merzsch, E. Uhde, A. Waag, and E. Peiner
J. Sens. Sens. Syst., 4, 111–123, https://doi.org/10.5194/jsss-4-111-2015, https://doi.org/10.5194/jsss-4-111-2015, 2015
Short summary
Short summary
The performance of a low-cost partially integrated cantilever-based airborne nanoparticle (NP) detector (CANTOR-1) is evaluated in terms of its real-time measurement and robustness. The device is used for direct reading of exposure to airborne carbon engineered nanoparticles (ENPs) in indoor workplaces.
S. G. Nedilko, S. L. Revo, V. P. Chornii, V. P. Scherbatskyi, and M. S. Nedielko
J. Sens. Sens. Syst., 4, 31–36, https://doi.org/10.5194/jsss-4-31-2015, https://doi.org/10.5194/jsss-4-31-2015, 2015
Short summary
Short summary
Microcrystalline cellulose, microcrystalline nitrite powders of common formulae MNO2, (M = Na, K) and two-component materials (cellulose + nitrite) have been prepared and characterized by means of optical microscopy and luminescence spectroscopy.The method of determining the nitrite compound traces via their sorption by cellulose using luminescent properties of the NO2- molecular ion has been developed and the low limit of NaNO2 determination in water solution was evaluated as 0.035 mg/l.
D. Puglisi, J. Eriksson, C. Bur, A. Schuetze, A. Lloyd Spetz, and M. Andersson
J. Sens. Sens. Syst., 4, 1–8, https://doi.org/10.5194/jsss-4-1-2015, https://doi.org/10.5194/jsss-4-1-2015, 2015
Short summary
Short summary
This study aims at the development of high-performance and cost-efficient gas sensors for sensitive detection of three specific hazardous gases, i.e., formaldehyde, naphthalene, and benzene, commonly present in indoor environments in concentrations of health concern. We used silicon carbide field effect transistors to investigate the sensor performance and characteristics under different levels of relative humidity up to 60%, demonstrating excellent detection limits in the sub-ppb range.
C. Bur, M. Bastuck, A. Schütze, J. Juuti, A. Lloyd Spetz, and M. Andersson
J. Sens. Sens. Syst., 3, 305–313, https://doi.org/10.5194/jsss-3-305-2014, https://doi.org/10.5194/jsss-3-305-2014, 2014
T. Mazingue, M. Lomello-Tafin, M. Passard, C. Hernandez-Rodriguez, L. Goujon, J.-L. Rousset, F. Morfin, and J.-F. Laithier
J. Sens. Sens. Syst., 3, 273–280, https://doi.org/10.5194/jsss-3-273-2014, https://doi.org/10.5194/jsss-3-273-2014, 2014
Short summary
Short summary
In this article, we propose detecting hydrogen (H2) traces at room temperature with nanostructured PdPt/Al2O3 catalysts. We measure the temperature rise during the exothermic oxidation of H2 by the catalyst. An appropriate formulation of about 1 mg of PdPt/Al2O3 leads to reversible thermal responses of 3°C in only 5 s. We show that this active material is a promising candidate for autonomous and reversible passive transducers for H2 sensors working at room temperature in explosive atmospheres.
M. Leidinger, T. Sauerwald, W. Reimringer, G. Ventura, and A. Schütze
J. Sens. Sens. Syst., 3, 253–263, https://doi.org/10.5194/jsss-3-253-2014, https://doi.org/10.5194/jsss-3-253-2014, 2014
Short summary
Short summary
An approach for detecting hazardous volatile organic compounds (VOCs) in ppb and sub-ppb concentrations is presented. Using metal oxide semiconductor (MOS) gas sensors in temperature cycled operation, VOCs in trace concentrations are successfully identified against a varying ethanol background of up to 2 ppm. For signal processing, linear discriminant analysis is applied to single sensor data and sensor fusion data. Integrated gas sensor systems using the same MOS sensors were characterized.
M. Bektas, D. Hanft, D. Schönauer-Kamin, T. Stöcker, G. Hagen, and R. Moos
J. Sens. Sens. Syst., 3, 223–229, https://doi.org/10.5194/jsss-3-223-2014, https://doi.org/10.5194/jsss-3-223-2014, 2014
F. P. Pentaris
J. Sens. Sens. Syst., 3, 145–165, https://doi.org/10.5194/jsss-3-145-2014, https://doi.org/10.5194/jsss-3-145-2014, 2014
B. Fabbri, S. Gherardi, A. Giberti, V. Guidi, and C. Malagù
J. Sens. Sens. Syst., 3, 61–67, https://doi.org/10.5194/jsss-3-61-2014, https://doi.org/10.5194/jsss-3-61-2014, 2014
Cited articles
Arroyo, P., Meléndez,, F., Suárez,, J. I., Herrero, J. L., Rodríguez,, S., and
Lozano, J.: Electronic Nose with Digital Gas Sensors Connected via Bluetooth
to a Smartphone for Air Quality Measurements, Sensors, 20, 786,
https://doi.org/10.3390/s20030786, 2020. a
AVIA Semiconductor: HX711 – 24-Bit Analog-to-Digital Converter (ADC) for
Weigh Scales, available at: https://datasheetspdf.com/pdf-file/842201/Aviasemiconductor/HX711/1
(last access: 19 November 2021), 2020. a
Ba̧k, B., Wilk, J., Artiemjew, P., Wilde, J., and Siuda, M.: Diagnosis of
Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas
Sensors, Sensors, 20, 4014, https://doi.org/10.3390/s20144014, 2020. a, b
Bosch: BME688 – Digital low power gas, pressure, temperature & humidity sensor
with AI, available at: https://www.bosch-sensortec.com/media/boschsensortec/downloads/datasheets/bst-bme688-ds000.pdf,
last access: 19 November 2021. a
Cecchi, S., Spinsante, S., Terenzi, A., and Orcioni, S.: A Smart Sensor-Based
Measurement System for Advanced Bee Hive Monitoring, Sensors, 20, 2726,
https://doi.org/10.3390/s20092726, 2020. a
Chazette, L., Becker, M., and Szczerbicka, H.: Basic algorithms for bee hive monitoring and laser-based mite control, IEEE Symposium Series on Computational Intelligence (SSCI), 2016, 1–8, https://doi.org/10.1109/SSCI.2016.7850001, 2016. a
Eric Mounier: Sensors and Sensing Modules for Smart Homes and Buildings –
2017 Report by Yole Developpement, available at: http://www.yole.fr/
(last access: 20 November 2021), 2017. a
Fukunaga, K.: Introduction to Statistical Pattern Recognition, Academic Press, 2 edn., ISBN 0-12-269851-7, 1990. a
Gil-Lebrero, S., Quiles-Latorre, F. J., Ortiz-López, M., Sánchez-Ruiz, V.,
Gómiz-López, V., and Luna-Rodríguez, J. J.: Honey Bee Colonies Remote
Monitoring System, Sensors, 17, 55, https://doi.org/10.3390/s17010055, 2017. a
Hudson, C. and Hudson, S.: Notes on Treatment Free Beekeeping, available at:
https://beemonitor.files.wordpress.com/2018/04/notes-on-treatment-free-beekeeping-jan-2018.pdf,
last access: 30 March 2020. a
IEEE: Conditioning Monitoring – A Decade of Proposed Techniques, IEEE
Ind. Electron. M., 9, 22–36, 2015. a
Jaeschke, C., Gonzalez, O., Padilla, M., Richardson, K., Glöckler, J.,
Mitrovics, J., and Mizaikoff, B.: A Novel Modular System for Breath Analysis
Using Temperature Modulated MOX Sensors, Proceedings, 14, 49,
https://doi.org/10.3390/proceedings2019014049, 2019. a, b
Kagermann, H., Lukas, W., and Wahlster, W.: Industrie 4.0: Mit dem
Internet der Dinge auf dem Weg zur 4. industriellen Revolution, Tech.
Rep. 13, VDI Nachrichten, available at: https://www.dfki.de/fileadmin/user_upload/DFKI/Medien/News_Media/Presse/Presse-Highlights/vdinach2011a13-ind4.0-Internet-Dinge.pdf (last access: 18 Januar 2022), 2011. a
Khoury, D. S., Barron, A. B., and Myerscough, M. R.: Modelling Food and
Population Dynamics in Honey Bee Colonies, PLOS ONE, 8, 1–7,
https://doi.org/10.1371/journal.pone.0059084, 2013. a
Knowles: SPH0645LM4H-B I2S Output Digital Microphone, available at: https://pdf1.alldatasheet.com/datasheet-pdf/view/791053/KNOWLES/SPH0645LM4H-B.html,
last access: 19 November 2021. a
Kohlert, M. and König, A.: Advanced multi-sensory process data analysis
and on-line evaluation by innovative human-machine-based process monitoring
and control for yield optimization in polymer film industry, TM–Tech.
Mess., 83, 474–483, https://doi.org/10.1515/teme-2015-0120, 2016. a
König, A.: IndusBee 4.0 – integrated intelligent sensory systems for
advanced bee hive instrumentation and hive keepers' assistance systems,
Sensors & Transducers, 237, 109–121, available at:
https://www.sensorsportal.com/HTML/DIGEST/september-october_2019/Vol_237/P_3118.pdf (last access: 18 Januar 2022),
2019. a, b, c, d
König, A.: 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, edited by:
Yurish, S. Y., vol. 2, chap. 8, IFSA Publishing, 1 edn., 443–463, available at: https://www.sensorsportal.com/HTML/BOOKSTORE/Advances_in_Signal_Processing_Vol_2.htm (last access: 18 Januar 2022),
2021a. a, b, c, d
König, A.: Cognitive Integrated Sensor Systems for In-Hive Varroa Infestation
Level Estimation based on Temperature-Modulated Gas Sensing, in: Sensor and
Measurement Science International (SMSI) 2021, chap. B4 Bio and Chemo Sensors AMA, Nuernberg, 127–128
https://doi.org/10.5162/SMSI2021/B4.3, 2021c. a
Kulyukin, V., Mukherjee, S., and Amlathe, P.: Toward Audio Beehive Monitoring:
Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio
Samples, Appl. Sci.-Basel, 8, 1573, https://doi.org/10.3390/app8091573, 2018. a, b
Lee, A. P. and Reedy, B. J.: Temperature modulation in semiconductor gas
sensing, Sensor. Actuat. B-Chem., 960, 35–42, 1999. a
Lee, J., Ghaffari, M., and Elmeligy, S.: Self-maintenance and engineering
immune systems: Towards smarter machines and manufacturing systems, Annu.
Rev. Control, 35, 111–122,
https://doi.org/10.1016/j.arcontrol.2011.03.007, 2011. a
Mallick, S.: LeanOpenCV – Blob Detection Using OpenCV (Python, C++),
available at: https://learnopencv.com/blob-detection-using-opencv-python-c/, last access: 19 November 2021. a
Mander, P.: Carnotcycle Blog – How to convert relative humidity to absolute
humidity, available at: https://carnotcycle.wordpress.com/2012/08/04/how-to-convert-relative-humidity-to-absolute-humidity/,
last access: 19 November 2021. a
Martinelli, E., Falconi, C., D'Amico, A., and Di Natale, C.: Feature
Extraction of chemical sensors in phase space, Sensors Actuator. B-Chem., 95, 132–139, https://doi.org/10.1016/S0925-4005(03)00422-2,
2003. a, b, c
Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. L., and Benetos,
E.: Audio-based identification of beehive states, CoRR, arXiv [preprint],
arXiv:1811.06330, 2018. a, b
Ohashi, M., Okada, R., Kimura, T., and Ikeno, H.: Observation system for the
control of the hive environment by the honeybee (Apis mellifera), Behav.
Res. Methods, 41, 782–786, https://doi.org/10.3758/BRM.41.3.782, 2009. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a, b, c
Pimoroni: BME680 – Python Library, GitHub [code], available at:
https://github.com/pimoroni/bme680-python (last access: 19 November 2021),
2020a. a
Pimoroni: SGP30 – Python Library, GitHub [code], available at:
https://github.com/pimoroni/sgp30-python (last access: 19 November 2021),
2020b. a
Rembert, L.: How AI and the IoT are improving farming sustainability, available at:
https://www.embedded.com/how-ai-and-the-iot-are-improving-farming-sustainability/, last access: 20 June 2020. a
Rüffer, D., Hoehne, F., and Bühler, J.: New Digital Metal-Oxide (MOx) Sensor
Platform, Sensors, 18, 1052, https://doi.org/10.3390/s18041052, 2018. a
Russell, S., Barron, A. B., and Harris, D.: Dynamic modelling of honey bee
(Apis mellifera) colony growth and failure, Ecol. Model., 265,
158–169, https://doi.org/10.1016/j.ecolmodel.2013.06.005, 2013. a
Sensry: Universal Sensor Platform – To Build Customized Industrial Sensor
Modules for Future IoT Applications, available at:
https://sensry.net/, last access: 9 July 2021. a
Suta, V. E. A.: Apiary Monitoring System, patent application WO
2015/048308 A1, available at:
https://patentimages.storage.googleapis.com/1a/4e/ec/afafdaeab9a9fd/WO2015048308A1.pdf (last access: 17 January 2022), 2014. a
Szczurek, A., Maciejewska, M., Ba̧k, B., Wilk, J., Wilde, J., and Siuda, M.:
Detection Level of Honeybee Desease: Varroosis Using a Gas Sensor Array, in: Proc. 5th Int. Conf. on Sensors and Electronic Instrumentation Advances (SEIA 2019), Canary Islands (Tenerife), Spain, 25–27 September 2019, available at: https://www.researchgate.net/publication/338854809_Detection_Level_of_Honeybee_Desease_Varroosis_Using_a_Gas_Sensor_Array/citation/download (last access: 17 January 2022), 2019. a
Szczurek, A., Maciejewska, M., Ba̧k, B., Wilk, J., Wilde, J., and Siuda, M.:
Detecting varroosis using a gas sensor system as a way to face the
environmental threat, Sci. Total Environ., 722, 137866,
https://doi.org/10.1016/j.scitotenv.2020.137866, 2020a.
a, b
Szczurek, A., Maciejewska, M., Zajiczek, Å., Ba̧k, B., Wilk, J., Wilde, J., and
Siuda, M.: The Effectiveness of Varroa destructor Infestation Classification
Using an E-Nose Depending on the Time of Day, Sensors, 20, 2532,
https://doi.org/10.3390/s20092532, 2020b. a
The SciPy community: Signal processing (scipy.signal) – Filtering,
The SciPy community [code], available at: https://docs.scipy.org/doc/scipy/reference/signal.html, last access: 19 November 2021. a
Umweltsensortechnik: Gas Sensors, Triple-Sensor, Datasheets, available at: http://www.umweltsensortechnik.de/en/gas-sensors/mox-gas-sensors-overview.html,
last access: 9 April 2021. a
Wallich, P.: Beehackers – Cheap widgets are like honey to hive keepers, IEEE
Spectrum, 48, 20–21, 2011. a
Weckbrodt, H.: Sensry Dresden horcht auf den Puls der Maschinen, available at:
https://oiger.de/2019/03/29/sensry-dresden-horcht-auf-den-puls-der-maschinen/170959 (last access: 20 November 2021),
2019. a
Werthschützky, R.: Sensor Technologien 2022, Tech. rep., AMA Verband für
Sensorik und Messtechnik e.V., available at: https://ama-sensorik.de/fileadmin/Pubikationen/180601-AMA-Studie-online-final.pdf (last access: 17 January 2022), 2018. a
Wimmer, W.: Praxishandbuch der thermischen Varroabekämpfung, available at:
https://www.varroa-controller.de/wp-content/uploads/2020/06/Handbook_German.pdf
(last access: 15 June 2021), 2020. a
Zak, M.: HX711 class for Rasperry Pi Zero, 2 and 3 written in Python 3, GitHub [code], available at:
https://github.com/gandalf15/HX711 (last access: 19 November 2021),
2020. a
Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z.: A New Deep Learning Model
for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw
Vibration Signals, Sensors, 17, 425, https://doi.org/10.3390/s17020425, 2017. a
Short summary
Bees play a major role in our ecosystem and the human food supply chain. Numerous threats, from pesticides to parasites, endanger bees and possibly cause bee colony collapse. The Varroa mite is one major parasite, and its timely detection and treatment is a key task for beekeepers. Contemporary sensors, electronics, and AI/PR allow vital parameters of bee hives to be monitored. Recent gas sensors (e.g., SGP30 or BME680) allow continuous in-hive parameter and Varroa population monitoring.
Bees play a major role in our ecosystem and the human food supply chain. Numerous threats, from...
Special issue