Search

Menu
Search
in Ecology

An intelligent monitoring system for forecasting and anomaly detection in precision beekeeping

45 Views


Abstract

Bees play a vital ecological role as pollinators, contributing to biodiversity, forest regeneration, and agricultural productivity. In recent years, precision beekeeping has emerged as a promising approach to support hive management through sensor-based monitoring. However, existing systems often lack predictive capabilities, limiting their usefulness in anticipating disruptive events that threaten colony health. To address this gap, we present BeeViz, an intelligent monitoring system that combines time series forecasting and anomaly detection to enhance decision-making in apiculture. The system integrates sensor networks, cloud infrastructure, and AI-based data processing modules to continuously track key hive parameters (temperature, humidity, and weight) and generate short-term forecasts and real-time alerts. Preliminary results show that the system can effectively detect anomalies and generate short-term forecasts for key hive parameters, with promising accuracy across different metrics. By enabling proactive interventions, BeeViz supports more resilient and sustainable beekeeping practices, paving the way for collaborative learning and data-driven hive management.

Data availability

The datasets used in this study originate from the HOBOS (HOneyBee Online Studies) project, which monitored honeybee hives equipped with various environmental and biometric sensors. The data, covering parameters such as temperature, humidity, and hive weight, are publicly available through Kaggle at the following URL: https://www.kaggle.com/datasets/se18m502/bee-hive-metrics. These datasets were collected between 2016 and 2019 from instrumented beehives in Germany. The use of these data complies with the terms of the Kaggle dataset license, and no additional permissions were required for academic use.

Code availability

The code developed for the BeeViz monitoring system, including modules for time series forecasting and anomaly detection, is available from the corresponding author upon reasonable request.

References

  1. FAO. Apimondia, CAAS, IZSLT: Good beekeeping practices for sustainable apiculture. In Technical Report, United Nations, Food and Agriculture Organization (FAO). https://openknowledge.fao.org/server/api/core/bitstreams/285dd834-945f-4964-8b7d-d2a4c5aab293/content (2021). Accessed 21 May 2025.

  2. European Parliament: Pollinator decline: causes, consequences and policy responses. https://www.europarl.europa.eu/pdfs/news/expert/2019/12/story/20191129STO67758/20191129STO67758_fr.pdf. Accessed Sep 2025 (2021).

  3. ANSES: Les abeilles, des pollinisateurs essentiels dont la santé est menacée. https://www.anses.fr/fr/content/les-abeilles-des-pollinisateurs-essentiels-dont-la-sante-est-menacee. Accessed Sep 2025 (2024).

  4. ADA. InterApi, ITSAP: Estimation de la production de miel 2024. In Technical Report, Federation of the Regional Beekeeping Development Associations (ADA France). https://www.adafrance.org/wp-content/uploads/2024/11/Estimation_Prod_Miel_2024.pdf. Accessed 21 May 2025 (2024).

  5. Kuan, Y. N., Goh, K. M. & Lim, L. L. Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques. Eng. Appl. Artif. Intell. 148, 110401. https://doi.org/10.1016/j.engappai.2025.110401 (2025).

    Google Scholar 

  6. Kriouile, Y., Ancourt, C., Wegrzyn-Wolska, K. & Bougueroua, L. Nested object detection using mask r-cnn: Application to bee and varroa detection. Neural Comput. Appl. 36(35), 22587–22609. https://doi.org/10.1007/s00521-024-10393-x (2024).

    Google Scholar 

  7. Verbeke, W. et al. European beekeepers’ interest in digital monitoring technology adoption for improved beehive management. Comput. Electron. Agric. 227, 109556. https://doi.org/10.1016/j.compag.2024.109556 (2024).

    Google Scholar 

  8. ITSAP. l.d.a. Enquête : les apiculteurs et le numérique. In Technical Report. https://itsap.asso.fr/articles/enquete-les-apiculteurs-et-le-numerique. Accessed 21 May 2025 (2020).

  9. Kady, C. et al. Iot-driven workflows for risk management and control of beehives. Diversity https://doi.org/10.3390/d13070296 (2021).

    Google Scholar 

  10. Zacepins, A., Stalidzans, E. & Meitalovs, J. Application of information technologies in precision apiculture. In Proceedings of the 13th International Conference on Precision Agriculture (ICPA 2012) (2012)

  11. Tashakkori, R., Hernandez, N.P., Ghadiri, A., Ratzloff, A.P. & Crawford, M.B. A honeybee hive monitoring system: From surveillance cameras to raspberry PIS. In SoutheastCon 2017. 1–7 (IEEE, 2017).

  12. Bjerge, K. et al. A computer vision system to monitor the infestation level of varroa destructor in a honeybee colony. Comput. Electron. Agr. 164, 104898. https://doi.org/10.1016/j.compag.2019.104898 (2019).

    Google Scholar 

  13. Magnier, B., Gabbay, E., Bougamale, F., Moradi, B., Pfister, F. & Slangen, P. Multiple honey bees tracking and trajectory modeling. In Multimodal Sensing: Technologies and Applications (Stella, E. ed.). Vol. 11059. 110590 (SPIE, 2019). https://doi.org/10.1117/12.2526120 (International Society for Optics and Photonics).

  14. Jeong, K. et al. IoT and AI systems for enhancing bee colony strength in precision beekeeping: A survey and future research directions. IEEE Internet Things 12(1), 362–389. https://doi.org/10.1109/JIOT.2024.3461775 (2025).

    Google Scholar 

  15. Cecchi, S., Spinsante, S., Terenzi, A. & Orcioni, S. A smart sensor-based measurement system for advanced bee hive monitoring. Sensors 20(9), 2726. https://doi.org/10.3390/s20092726 (2020).

    Google Scholar 

  16. Abou-Shaara, H., Owayss, A., Ibrahim, Y. & Basuny, N. A review of impacts of temperature and relative humidity on various activities of honey bees. Insect. Soc. 64, 455–463. https://doi.org/10.1007/s00040-017-0573-8 (2017).

    Google Scholar 

  17. Singh, I. & Singh, S. Honey moisture reduction and its quality. J. Food Sci. Technol. 55(10), 3861–3871. https://doi.org/10.1007/s13197-018-3341-5 (2018).

    Google Scholar 

  18. Polatidou, K., Nouska, C., Tananaki, C., Biliaderis, C. G. & Lazaridou, A. Physicochemical and rheological characteristics of monofloral honeys-kinetics of creaming-crystallization. Foods https://doi.org/10.3390/foods14101835 (2025).

    Google Scholar 

  19. Ochoa, I.Z., Gutierrez, S. & Rodríguez, F. Internet of things: Low cost monitoring beehive system using wireless sensor network. In 2019 IEEE International Conference on Engineering Veracruz (ICEV). Vol. 1. 1–7 (IEEE, 2019).

  20. Robustillo, M. C., Pérez, C. J. & Parra, M. I. Predicting internal conditions of beehives using precision beekeeping. Biosyst. Eng. 221, 19–29. https://doi.org/10.1016/j.biosystemseng.2022.06.006 (2022).

    Google Scholar 

  21. Erdal, B. & Tolga, T. Time series forecasting of honey production in Turkey. Avrupa Bilim Teknol. Dergisi 35, 417–423. https://doi.org/10.31590/ejosat.1066665 (2022).

    Google Scholar 

  22. Anwar, O., Keating, A., Cardell-Oliver, R., Datta, A. & Putrino, G. We-bee: Weight estimator for beehives using deep learning. In 36th AAAI Conference on Artificial Intelligence: AAAI-22 (2022)

  23. Meireles Filho, M. & Sousa, A. et al. Application of artificial intelligence through neural networks to predict honey production based on weather patterns. In ENCOINFO-Congresso de Computação e Tecnologias da Informação. 15–21 (ENCOINFO, 2021).

  24. Zacepins, A., Kviesis, A., Stalidzans, E., Liepniece, M. & Meitalovs, J. Remote detection of the swarming of honey bee colonies by single-point temperature monitoring. Biosyst. Eng. 148, 76–80. https://doi.org/10.1016/j.biosystemseng.2016.05.012 (2016).

  25. Rubin-Delanchy, P., Lawson, D.J. & Heard, N.A. Anomaly detection for cyber security applications. In Dynamic Networks and Cyber-security. 137–156. https://doi.org/10.1142/q0022 (World Scientific, 2016).

  26. Chen, S.-H., Wang, J.-C., Lin, H.-J., Lee, M.-H., Liu, A.-C., Wu, Y.-L., Hsu, P.-S., Yang, E.-C. & Jiang, J.-A. A machine learning-based multiclass classification model for bee colony anomaly identification using an IOT-based audio monitoring system with an edge computing framework. Expert Syst. Appl. 255, 124898. https://doi.org/10.1016/j.eswa.2024.124898 (2024).

  27. Davidson, P., Steininger, M., Lautenschlager, F., Krause, A. & Hotho, A. Anomaly detection in beehives: An algorithm comparison. In International Conference on Sensor Networks. 1–20 (Springer, 2020).

  28. Senger, D., Johannsen, C., Melemenidis, A., Goncharskiy, A. & Kluss, T. Unsupervised anomaly detection on multisensory data from honey bee colonies. In 2020 IEEE International Conference on Data Mining (ICDM). 1238–1243 (IEEE, 2020).

  29. Metidji, S.A., Huet, J.-C. & Bougueroua, L. Data preparation for prediction and anomaly detection in precision beekeeping. Proc. Comput. Sci. 246, 4008–4017. https://doi.org/10.1016/j.procs.2024.09.175 (2024) (28th international conference on knowledge based and intelligent information and engineering systems, KES, 2024).

  30. Robustillo, M.C., Naranjo, L., Parra, M.I. & Pérez, C.J. Addressing multidimensional highly correlated data for forecasting in precision beekeeping. Comput. Electron. Agric. 226, 109390. https://doi.org/10.1016/j.compag.2024.109390 (2024).

  31. Zhao, Y., Liu, Y. & Zhang, Y. et al. Deepautod: Research on distributed machine learning oriented scalable mobile communication security unpacking system. In IEEE Transactions on Network and Service Management. Vol. 18. 1234–1247. https://doi.org/10.1109/tnse.2021.3100750 (2023).

  32. Tan, Y., Huang, W., You, Y., Su, S. & Lu, H. Recognizing bgp communities based on graph neural network. IEEE Netw. https://doi.org/10.1109/mnet.2024.3414113 (2024).

    Google Scholar 

  33. Hu, W., Gu, Z., Zhang, C., Wang, L. & Tang, K. Adversarial examples generation system based on gradient shielding of restricted region. In Artificial Intelligence and Security (ICAIS). Communications in Computer and Information Science. Vol. 1254. 81–91. https://doi.org/10.1007/978-981-15-8101-4_9 (2020).

  34. Zhang, Y., Yang, S., Xu, L., Li, X. & Zhao, D. A malware detection framework based on semantic information of behavioral features. Appl. Sci. 13(22), 12528. https://doi.org/10.3390/app132212528 (2023).

    Google Scholar 

  35. Li, J., Lai, S. & Shuai, Z. et al. A comprehensive review of community detection in graphs. arXiv preprint arXiv:2309.11798. https://doi.org/10.48550/arXiv.2309.11798 (2023).

  36. Wang, T., Bi, Z. & Zhang, Y. et al. Deep learning model security: Threats and defenses. arXiv preprint arXiv:2412.08969. https://doi.org/10.48550/arXiv.2412.08969 (2024).

Download references

Author information

Authors and Affiliations

  1. EFREI Research Lab, Université Paris-Panthéon-Assas, 30-32 Avenue de la République, 94800, Villejuif, France

    Jean-Charles Huet, Lamine Bougueroua & Sid Ahmed Metidji

Authors

  1. Jean-Charles Huet
    View author publications

    Search author on:PubMed Google Scholar

  2. Lamine Bougueroua
    View author publications

    Search author on:PubMed Google Scholar

  3. Sid Ahmed Metidji
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.C.H. and L.B. contributed equally to the conceptualization, validation, original draft writing, review and editing, visualization, supervision, and project administration. S.A.M. was responsible for formal analysis, investigation, data curation, and contributed to the review and editing of the manuscript. All authors have read and approved the final version of the manuscript.

Corresponding author

Correspondence to
Jean-Charles Huet.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Huet, JC., Bougueroua, L. & Metidji, S.A. An intelligent monitoring system for forecasting and anomaly detection in precision beekeeping.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-37877-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-026-37877-1

Keywords

  • Agroecology
  • Precision beekeeping
  • Hive management
  • Intelligent forecasting
  • Anomaly detection

Source: Ecology - nature.com

Integrated action for skin NTDs: Deconstructing transmission, addressing knowledge gaps, and championing one health strategies

Critical classification parameters linking species to Plant Functional Type in African ecosystems

This portal is not a newspaper as it is updated without periodicity. It cannot be considered an editorial product pursuant to law n. 62 of 7.03.2001. The author of the portal is not responsible for the content of comments to posts, the content of the linked sites. Some texts or images included in this portal are taken from the internet and, therefore, considered to be in the public domain; if their publication is violated, the copyright will be promptly communicated via e-mail. They will be immediately removed.

Back to Top
Close