Intelligent system for monitoring physical variables in beehives using neural networks
DOI:
https://doi.org/10.61117/ipsumtec.v9i1.466Keywords:
Beehives, IoT, Neural networks, Monitoring system, Physical variablesAbstract
Beekeeping activities are important not just for honey but also for pollinating plants. In Mexico, it is a relevant economic activity, and the country is placed as the ninth biggest honey producer on the world. In order to keep the hives in good health and for effective output there is a need for constant monitoring. Traditionally, this process is made by checking hives manually every 8 to 15 days to see any problems or sickness. As the number of hives rises, this kind of monitoring becomes inefficient, time consuming and more difficult in planning and can even mean some hives are not checked at the right time. This article presents a system that checks hives using Internet of Things technology for monitoring. It integrates sensors that watch over variables such as the hive temperature, wetness, weight and sound; and a pattern recognition algorithm to detect important events needing attention from the beekeeper. The system includes a mobile app which can display the alerts and the sensors output. The purpose of this project is to help beekeepers get instant data about their hive status, aiding their choices and management.
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J. M. Flores and J. A. Ruiz, “Principios básicos de la apicultura moderna,” Revista de Ciencias Agropecuarias, vol. 38, no. 2, 2021, doi: 10.22201/fesz.23958723e.2021.2.82.
A. Correa Ben’{i}tez, I. Vásquez Valencia, N. Peña Haaz, L. León Luna, and R. Anguiano Báez, “Buenas prácticas pecuarias en la pro-ducción primaria de miel,” 2018. [Online]. Available: https://atlas-abe-jas.agricultura.gob.mx/pdfs/Manual_BPP_en_la_Producci_n_primaria_de_Miel_octubre_2018.pdf
A.-M. Klein et al., “Importance of pollinators in changing landscapes for world crops,” Proceed-ings of the Royal Society B: Biological Scienc-es, vol. 274, no. 1608, 2007, doi: 10.1098/rspb.2006.3721. DOI: https://doi.org/10.1098/rspb.2006.3721
S. Cecchi, S. Spinsante, A. Terenzi, and S. Or-cioni, “A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring,” Sensors, vol. 20, no. 9, p. 2726, May 2020, doi: 10.3390/s20092726. DOI: https://doi.org/10.3390/s20092726
M. Vidrascu and D. Svasta, “Embedded Soft-ware for IoT Bee Hive Monitoring Node,” 2017. DOI: https://doi.org/10.1109/SIITME.2017.8259887
M. Vidrascu and D. Svasta, “Maintenance-free IoT Gateway Design for Bee Hive Monitoring,” 2017. DOI: https://doi.org/10.1109/SIITME.2017.8259886
M. Vidrascu, P. Svasta, and M. Vladescu, “High reliability wireless sensor node for bee hive mon-itoring,” Sensors and Actuators, pp. 134–138, Mar. 2016, doi: 10.1109/SIITME.2016.7777262. DOI: https://doi.org/10.1109/SIITME.2016.7777262
“MinIO High Performance Object Storage — MinIO Object Storage (AGPLv3).” Accessed: Mar. 19, 2026. [Online]. Available: https://minio-docs.tf.fo/
B. McFee et al., “librosa: Audio and Music Sig-nal Analysis in Python,” J. Open Source Softw., 2015, doi: 10.21105/joss.00022. DOI: https://doi.org/10.25080/Majora-7b98e3ed-003
M. Müller, Fundamentals of Music Processing. Springer, 2015. Doi: 10.1007/978-3-319-21945-5. DOI: https://doi.org/10.1007/978-3-319-21945-5
G. Tzanetakis and P. Cook, “Musical Genre Classification of Audio Signals,” IEEE Transac-tions on Speech and Audio Processing, vol. 10, no. 5, 2002, doi: 10.1109/TSA.2002.800560. DOI: https://doi.org/10.1109/TSA.2002.800560
T. Giannakopoulos and A. Pikrakis, Introduc-tion to Audio Analysis: A MATLAB Approach. Academic Press, 2014. [Online]. Available: https://www.mathworks.com/academia/books/introduction-to-audio-analysis-giannkopoulos.html
L. Rabiner and B.-H. Juang, Fundamentals of Speech Recognition. Prentice Hall, 2010. DOI: https://doi.org/10.1201/9781420046090-c9
X. Li, K. Lee, J. Park, and S. Cho, “Audio fea-ture extraction and classification using entropy-based methods,” IEEE Trans. Multimedia, vol. 19, no. 2, 2017.
G. Peeters, “A large set of audio features for sound description (similarity and classification) in the CUIDADO project,” Paris, 2004. [Online]. Available: https://recherche.ircam.fr/anasyn/peeters/ARTICLES/Peeters_2003_cuidadoaudiofeatures.pdf
D. Jiang and others, “Spectral entropy for robust speech recognition,” ICASSP, 2011.
J. Foote, “Automatic Audio Segmentation using a Measure of Audio Novelty,” in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME), New York, USA, 2000. doi: 10.1109/ICME.2000.869637. DOI: https://doi.org/10.1109/ICME.2000.869637
S. Davis and P. Mermelstein, “Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sen-tences,” IEEE Trans. Acoust, vol. 28, no. 4, 1980, doi: 10.1109/TASSP.1980.1163420. DOI: https://doi.org/10.1109/TASSP.1980.1163420
M. Slaney, “Auditory Toolbox Version 2,” 1998. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/AuditoryToolboxTechReport.pdf
B. Logan, “Mel Frequency Cepstral Coefficients for Music Modeling,” in Proceedings of the 1st International Symposium on Music Information Retrieval (ISMIR), Plymouth, MA, USA, 2000. [Online]. Available: https://ismir2000.ismir.net/papers/logan_abs.pdf
V. Kulyukin, S. Mukherjee, and P. Amlathe, “Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples,” Applied Sciences, vol. 8, no. 9, p. 1573, Sep. 2018, doi: 10.3390/app8091573. DOI: https://doi.org/10.3390/app8091573
J. A. Calvo, “Open Source Beehives Project - Audio Database as of 2-21-17,” 2017. doi: 10.5281/zenodo.321345.
icarodelimarodrigues, “Queen Loss Event in Africanized Honeybee Colony.” [Online]. Available: https://www.kaggle.com/datasets/icarodelimarodrigues/queen-loss-event-in-africanized-honeybee-colony
yevheniiklymenko, “Beehive buzz anomalies,” 2021. [Online]. Available: https://www.kaggle.com/datasets/yevheniiklymenko/beehive-buzz-anomalies
C. Montaño, “Bumblebee Repository,” 2026. [Online]. Available: https://github.com/carlos-montano-hub/bumblebee
A. Zgank, «Bee Swarm Activity Acoustic Classi-fication for an IoT-Based Farm Service,»
Sensors, vol. 20, dic. de 2019. DOI: 10.3390/s20010021. DOI: https://doi.org/10.3390/s20010021
