Journals / Türkiye Teknoloji ve Uygulamalı Bilimler Dergisi / 2025 / Cilt: 6 Sayı: Özel Sayı Özel Sayı

Technological Applications and Future Perspectives in Precision Beekeeping

Hassas Arıcılıkta Teknolojik Uygulamalar ve Gelecek Perspektifleri

Muhammed Hasan Şirin(ANKARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ ENSTİTÜSÜ, FARMAKOLOJİ VE TOKSİKOLOJİ (DR) (VETERİNER))0000-0001-6451-3663
Sedat Sevin(ANKARA ÜNİVERSİTESİ, VETERİNER FAKÜLTESİ, KLİNİK ÖNCESİ BİLİMLERİ BÖLÜMÜ, VETERİNERLİK FARMAKOLOJİ VE TOKSİKOLOJİSİ ANABİLİM DALI)0000-0003-0475-9092
Pages
21–33
Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Ultimately, Precision Beekeeping aims to ensure the sustainability of bee colonies, which play a vital role in global food security, by making beekeeping practices more efficient and proactive.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Precision Beekeeping is a data-driven management strategy that goes beyond the boundaries of traditional beekeeping, offering a technology-based solution to the global decline in bee populations.
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Key Findings & Data:These advanced analyses provide beekeepers with early warnings and highly accurate predictions (up to 99%) regarding swarming tendencies, queen presence, honey yield, and early detection of pests such as Varroa destructor.
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Conclusion & Impact:Ultimately, Precision Beekeeping aims to ensure the sustainability of bee colonies, which play a vital role in global food security, by making beekeeping practices more efficient and proactive.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Akıllı Kovan Sistemleri · Hassas Arıcılık · Koloni Sağlığı

Abstract

Precision Beekeeping is a data-driven management strategy that goes beyond the boundaries of traditional beekeeping, offering a technology-based solution to the global decline in bee populations. Its main goal is to continuously monitor each colony using non-invasive methods to minimize resource consumption and maximize bee health and productivity. Data collection in Precision Beekeeping is structured on three levels: Apiary-level parameters (e.g., meteorological and environmental video monitoring), Colony-level parameters (e.g., temperature, weight, humidity, sound/vibration, gases, and infrared imaging), and Individual bee parameters (e.g., entry/exit counts and behavior tracking). The integration of Smart Hive Systems uses IoT sensors to feed data to Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advanced analyses provide beekeepers with early warnings and highly accurate predictions (up to 99%) regarding swarming tendencies, queen presence, honey yield, and early detection of pests such as Varroa destructor. Ultimately, Precision Beekeeping aims to ensure the sustainability of bee colonies, which play a vital role in global food security, by making beekeeping practices more efficient and proactive.

Özet

Hassas Arıcılık, geleneksel arıcılığın sınırlarının ötesine geçerek, arı popülasyonlarındaki küresel düşüşe teknoloji tabanlı bir çözüm sunan veri odaklı bir yönetim stratejisidir. Ana amacı, kaynak tüketimini en aza indirerek arı sağlığı ve üretkenliğini en üst düzeye çıkarmak için her koloniyi invaziv olmayan yöntemlerle sürekli olarak izlemektir. Hassas Arıcılıkda veri toplama üç düzeyde yapılandırılmıştır: Arılık düzeyindeki parametreler (örneğin, meteorolojik ve çevresel video izleme), Koloni düzeyindeki parametreler (örneğin, sıcaklık, ağırlık, nem, ses/titreşim, gazlar ve kızılötesi görüntüleme) ve Bireysel arı parametreleri (örneğin, giriş/çıkış sayıları ve davranış takibi). Akıllı Kovan Sistemlerinin entegrasyonu, Yapay Zeka (AI) ve Makine Öğrenmesi (ML) algoritmalarına veri beslemek için IoT sensörlerini kullanmaktadır. Bu gelişmiş analizler, arıcıların oğul verme eğilimleri, ana arı varlığı, bal verimi ve Varroa destructor gibi zararlıların erken tespiti konusunda erken uyarılar ve yüksek doğruluklu tahminler (%99'a kadar) almalarını sağlamaktadır. Sonuç olarak, Hassas Arıcılık, arıcılık uygulamalarını daha verimli ve önleyici hale getirerek küresel gıda güvenliğinde hayati bir rol oynayan arı kolonilerinin sürdürülebilirliğini sağlamayı amaçlamaktadır.

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Cite This Article

Şirin, M. H., Sevin, S. (2025). Technological Applications and Future Perspectives in Precision Beekeeping. Türkiye Teknoloji ve Uygulamalı Bilimler Dergisi, Cilt: 6 Sayı: Özel Sayı Özel Sayı, 21–33. https://doi.org/10.70562/tubid.1814350

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References (51)

  1. Potts SG, Imperatriz-Fonseca VL, Ngo HT, Biesmeijer JC, Breeze TD, Dicks LV, et al. Summary for policymakers of the assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) on pollinators, pollination and food production. Bonn (Germany): IPBES Secretariat; 2016.
  2. Food and Agriculture Organization of the United Nations (FAO). Why bees matter: the importance of bees and other pollinators for food and agriculture. Rome: FAO; 2018
  3. Tsvetanov T. Precision beekeeping: essence and systems in the transfer of information in the manufacture. Bulgarian Journal of Animal Husbandry/Životnov Dni Nauki. 2022;59(3).
  4. Becher MA, Osborne JL, Thorbek P, Kennedy PJ, Grimm V. Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models. Journal of Applied Ecology. 2013;50(4):868-80.
  5. Insolia L, Molinari R, Rogers SR, Williams GR, Chiaromonte F, Calovi M. Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States. Scientific reports. 2022;12(1):20787.
  6. Kviesis A, Zacepins A. System architectures for real-time bee colony temperature monitoring. Procedia Computer Science. 2015;43:86-94.
  7. Gil-Lebrero S, Quiles-Latorre FJ, Ortiz-López M, Sánchez-Ruiz V, Gámiz-López V, Luna-Rodríguez JJ. Honey bee colonies remote monitoring system. Sensors. 2016;17(1):55.
  8. Zacepins A, Brusbardis V, Meitalovs J, Stalidzans E. Challenges in the development of Precision Beekeeping. Biosystems Engineering. 2015;130:60-71.
  9. Šabić J, Perković T, Šolić P, Šerić L. Buzzing with Intelligence: A Systematic Review of Smart Beehive Technologies. Sensors. 2025;25(17):5359.
  10. Alleri M, Amoroso S, Catania P, Verde GL, Orlando S, Ragusa E, et al. Recent developments on precision beekeeping: A systematic literature review. Journal of Agriculture and Food Research. 2023;14:100726.
  11. Zacepins A, Stalidzans E, Meitalovs J. Application of information technologies in precision apiculture. In: Proceedings of the International Conference on Precision Agriculture (ICPA 2012); 2012 Jul 15-18; Indianapolis (IN), USA. (Sayfa no. eklenebiliyorsa ekleyin.)
  12. Meikle W, Holst N. Application of continuous monitoring of honeybee colonies. Apidologie. 2015;46(1):10-22.
  13. Human H, Brodschneider R, Dietemann V, Dively G, Ellis JD, Forsgren E, et al. Miscellaneous standard methods for Apis mellifera research. Journal of Apicultural Research. 2013;52(4):1-53.
  14. Meitalovs J, Histjajevs A, Stalidzans E. Automatic microclimate controlled beehive observation system. In: Proceedings of the 8th International Scientific Conference “Engineering for Rural Development”; 2009; Jelgava, Latvia. p. 265-271.
  15. Catania P, Vallone M. Application of a precision apiculture system to monitor honey daily production. Sensors. 2020;20(7):2012.
  16. Komasilova O, Komasilovs V, Kviesis A, Bumanis N, Mellmann H, Zacepins A. Model for the bee apiary location evaluation. Agronomy Research. 2020;18(S2):1350-1358.
  17. Marković D, Pešović U, Ðurašević S, Ranđić S. Decision support system for temperature monitoring in beehives. Acta Agriculturae Serbica. 2016;21:135-144.
  18. Zacepins A, Karasha T. Application of temperature measurements for bee colony monitoring: a review. In: Proceedings of the 12th International Scientific Conference “Engineering for Rural Development”; 2013 May 23-24; Jelgava, Latvia. p. 126-131.
  19. Hadjur H, Ammar D, Lefèvre L. Toward an intelligent and efficient beehive: A survey of precision beekeeping systems and services. Computers and Electronics in Agriculture. 2022;192:106604.
  20. Meikle WG, Rector BG, Mercadier G, Holst N. Within-day variation in continuous hive weight data as a measure of honey bee colony activity. Apidologie. 2008;39(6):694-707.
  21. Danieli PP, Addeo NF, Lazzari F, Manganello F, Bovera F. Precision beekeeping systems: State of the art, pros and cons, and their application as tools for advancing the beekeeping sector. Animals. 2023;14(1):70.
  22. Robles-Guerrero A, Saucedo-Anaya T, Gonzalez E, de la Rosa JI. Queenless honeybee acoustic patterns [dataset]. Mendeley Data. 2022;V1. doi:10.17632/t9prmbmdfn.1.
  23. Bencsik M, Bencsik J, Baxter M, Lucian A, Romieu J, Millet M. Identification of the honey bee swarming process by analysing the time course of hive vibrations. Computers and electronics in agriculture. 2011;76(1):44-50.
  24. Jarimi H, Tapia-Brito E, Riffat S. A review on thermoregulation techniques in honey bees’ (Apis mellifera) beehive microclimate and its similarities to the heating and cooling management in buildings. Future Cities and Environment. 2020;6:8. doi:10.5334/fce.81.
  25. Abou-Shaara HF, Al-Ghamdi AA, Mohamed AA. Tolerance of two honey bee races to various temperature and relative humidity gradients. Environmental and experimental Biology. 2012;10(4):133-8.
  26. Urban M, Chlebo R. Current Status and Future Outlooks of Precision Beekeeping Systems and Services. Reviews in Agricultural Science. 2024;12:165-81.
  27. Szczurek A, Maciejewska M. Beehive air sampling and sensing device operation in apicultural applications—methodological and technical aspects. Sensors. 2021;21(12):4019.
  28. Edwards-Murphy F, Magno M, Whelan PM, O’Halloran J, Popovici EM. b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring. Computers and Electronics in Agriculture. 2016;124:211-9.
  29. Mitrus S. Emigration speed and the production of sexuals in colonies of the ant Temnothorax crassispinus under high and low levels of disturbance. Insectes sociaux. 2016;63(1):127-34.
  30. Odemer R. Approaches, challenges and recent advances in automated bee counting devices: A review. Annals of Applied Biology. 2022;180(1):73-89.
  31. Linton F. Hive monitoring technology: high tech hives. American Bee Journal. 2012;152(8):767.
  32. Yang C, Collins J. Deep learning for pollen sac detection and measurement on honeybee monitoring video. In: 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ); 2019 Dec 2-4; Dunedin, New Zealand. p. 1-6. doi:10.1109/IVCNZ48456.2019.8961011.
  33. Crawford E, Leidenberger S, Norrström N, Niklasson M. Using video footage for observing honey bee behaviour at hive entrances. Bee World. 2022;99(4):139-42.
  34. Kulyukin V, Mukherjee S, Amlathe P. Toward audio beehive monitoring: Deep learning vs. standard machine learning in classifying beehive audio samples. Applied Sciences. 2018;8(9):1573.
  35. Schurischuster S, Remeseiro B, Radeva P, Kampel M. A preliminary study of image analysis for parasite detection on honey bees. In: Campilho A, Karray F, ter Haar Romeny BM, editors. Image Analysis and Recognition (ICIAR 2018). Lecture Notes in Computer Science. Vol 10882. Cham: Springer; 2018. p. 465-473. doi:10.1007/978-3-319-93000-8_52.
  36. Sevin S, Tutun H, Mutlu S. Detection of varroa mites from honey bee hives by smart technology var-gor: A hivemonitoring and image processing device. Turkish Journal of Veterinary & Animal Sciences. 2021;45(3):487-91.
  37. Turyagyenda A, Katumba A, Akol R, Nsabagwa M, Mkiramweni ME. IoT and Machine Learning Techniques for Precision Beekeeping: A Review. AI. 2025;6(2):26.
  38. Jeong K, Oh H, Lee Y, Seo H, Jo G, Jeong J, et al. IoT and AI systems for enhancing bee colony strength in precision beekeeping: a survey and future research directions. IEEE Internet of Things Journal. 2024;12(1):362-89.
  39. De Simone A, Barbisan L, Turvani G, Riente F. Advancing beekeeping: IoT and TinyML for queen bee monitoring using audio signals. IEEE Transactions on Instrumentation and Measurement. 2024;73:1-9. doi:10.1109/TIM.2024.3449981.
  40. Iqbal K, Alabdullah B, Al Mudawi N, Algarni A, Jalal A, Park J. Empirical analysis of honeybees acoustics as biosensors signals for swarm prediction in beehives. IEEE Access. 2024;12:148405-148421. doi:10.1109/ACCESS.2024.3471895.
  41. Braga AR, Gomes DG, Rogers R, Hassler EE, Freitas BM, Cazier JA. A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies. Computers and Electronics in Agriculture. 2020;169:105161.
  42. Liang A. Developing a Multimodal System for Bee Object Detection and Health Assessment. IEEE Access. 2024;12:158703-13.
  43. Robles-Guerrero A, Saucedo-Anaya T, Guerrero-Mendez CA, Gómez-Jiménez S, Navarro-Solís DJ. Comparative study of machine learning models for bee colony acoustic pattern classification on low computational resources. Sensors. 2023;23(1):460.
  44. Vdoviak G, Sledevič T, Serackis A, Plonis D, Matuzevičius D, Abromavičius V. Evaluation of Deep Learning Models for Insects Detection at the Hive Entrance for a Bee Behavior Recognition System. Agriculture. 2025;15(10):1019.
  45. Rozenbaum E, Shrot T, Daltrophe H, Kunya Y, Shafir S. Machine learning-based bee recognition and tracking for advancing insect behavior research. Artificial Intelligence Review. 2024;57(9):245.
  46. Bilik S, Zemcik T, Kratochvila L, Ricanek D, Richter M, Zambanini S, et al. Machine learning and computer vision techniques in continuous beehive monitoring applications: A survey. Computers and Electronics in Agriculture. 2024;217:108560.
  47. Mrozek D, Gȯrny R, Wachowicz A, Małysiak-Mrozek B. Edge-based detection of varroosis in beehives with iot devices with embedded and tpu-accelerated machine learning. Applied Sciences. 2021;11(22):11078.
  48. Chen S-H, Wang J-C, Lin H-J, Lee M-H, Liu A-C, Wu Y-L, et al. 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 Systems with Applications. 2024;255:124898.
  49. Degenfellner J, Templ M. Modeling bee hive dynamics: Assessing colony health using hive weight and environmental parameters. Computers and Electronics in Agriculture. 2024;218:108742.
  50. Albayrak A, Çeven S, Bayır R. Modeling of migratory beekeeper behaviors with machine learning approach using meteorological and environmental variables: The case of Turkey. Ecological Informatics. 2021;66:101470.
  51. Kamga GAF, Bouroubi Y, Germain M, Martin G, Bitjoka L. Beekeeping suitability prediction based on an adaptive neuro-fuzzy inference system and apiary level data. Ecological Informatics. 2025;86:103015.