{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:09:05Z","timestamp":1791914945612,"version":"4.3.4"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Javna Agencija za Raziskovalno Dejavnost RS","doi-asserted-by":"publisher","award":["P2-0069"],"award-info":[{"award-number":["P2-0069"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["IQdom"],"award-info":[{"award-number":["IQdom"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Beekeeping is one of the widespread and traditional fields in agriculture, where Internet of Things (IoT)-based solutions and machine learning approaches can ease and improve beehive management significantly. A particularly important activity is bee swarming. A beehive monitoring system can be applied for digital farming to alert the user via a service about the beginning of swarming, which requires a response. An IoT-based bee activity acoustic classification system is proposed in this paper. The audio data needed for acoustic training was collected from the Open Source Beehives Project. The input audio signal was converted into feature vectors, using the Mel-Frequency Cepstral Coefficients (with cepstral mean normalization) and Linear Predictive Coding. The influence of the acoustic background noise and denoising procedure was evaluated in an additional step. Different Hidden Markov Models\u2019 and Gaussian Mixture Models\u2019 topologies were developed for acoustic modeling, with the objective being to determine the most suitable one for the proposed IoT-based solution. The evaluation was carried out with a separate test set, in order to successfully classify sound between the normal and swarming conditions in a beehive. The evaluation results showed that good acoustic classification performance can be achieved with the proposed system.<\/jats:p>","DOI":"10.3390\/s20010021","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Bee Swarm Activity Acoustic Classification for an IoT-Based Farm Service"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1411-5293","authenticated-orcid":false,"given":"Andrej","family":"Zgank","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.mcm.2010.11.053","article-title":"Development of a smart mobile farming service system","volume":"54","author":"Zheng","year":"2011","journal-title":"Math. Comput. Model."},{"key":"ref_2","unstructured":"Ryu, M., Yun, J., Miao, T., Ahn, I.Y., Choi, S.C., and Kim, J. (2015, January 1\u20134). Design and implementation of a connected farm for smart farming system. Proceedings of the 2015 IEEE SENSORS, Busan, Korea."},{"key":"ref_3","first-page":"10","article-title":"Smart computing and sensing technologies for animal welfare: A systematic review","volume":"50","author":"Jukan","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jayaraman, P., Yavari, A., Georgakopoulos, D., Morshed, A., and Zaslavsky, A. (2016). Internet of things platform for smart farming: Experiences and lessons learnt. Sensors, 16.","DOI":"10.3390\/s16111884"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big data in smart farming\u2014A review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_6","unstructured":"Berckmans, D. (2004, January 11\u201313). Automatic on-line monitoring of animals by precision livestock farming. Proceedings of the International Congress on Animal Production in Europe, Saint-Malo, France."},{"key":"ref_7","unstructured":"Zacepins, A., Kviesis, A., Ahrendt, P., Richter, U., Tekin, S., and Durgun, M. (June, January 29). Beekeeping in the future\u2014Smart apiary management. Proceedings of the 17th International Carpathian Control Conference (ICCC), Tatranska Lomnica, Slovakia."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biosystemseng.2014.12.001","article-title":"Challenges in the development of Precision Beekeeping","volume":"130","author":"Zacepins","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zgank, A. (2018, January 21\u201323). Acoustic monitoring and classification of bee swarm activity using MFCC feature extraction and HMM acoustic modeling. Proceedings of the ELEKTRO Conference, Mikulov, Czech Republic.","DOI":"10.1109\/ELEKTRO.2018.8398253"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Panayotov, V., Chen, G., Povey, D., and Khudanpur, S. (2015, January 19\u201324). Librispeech: An ASR corpus based on public domain audio books. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia.","DOI":"10.1109\/ICASSP.2015.7178964"},{"key":"ref_11","unstructured":"De Jong, F.M.G., Maegaard, B., De Smedt, K., Fi\u0161er, D., and Van Uytvanck, D. (2018, January 7\u201312). CLARIN: Towards FAIR and responsible data science using language resources. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan."},{"key":"ref_12","unstructured":"Boys, R. (2019, December 18). Beekeeping in a Nutshell\u2014Listen to the Bees. Self Published Work. Available online: https:\/\/beedata.com.mirror.hiveeyes.org\/data2\/listen\/listenbees.htm."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1080\/00218839.1985.11100668","article-title":"A Method for Remote Monitoring of Activity of Honeybee Colonies by Sound Analysis","volume":"24","author":"Dietlein","year":"1985","journal-title":"J. Apic. Res."},{"key":"ref_14","first-page":"615","article-title":"Using acoustic technology to monitor your hives","volume":"135","author":"Vancata","year":"1995","journal-title":"Am. Bee J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compag.2008.05.010","article-title":"Monitoring of swarming sounds in bee hives for early detection of the swarming period","volume":"64","author":"Ferrari","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"290","article-title":"Signal processing the acoustics of honeybees (APIS MELLIFERA) to identify the \u201cqueenless\u201d state in Hives","volume":"35","author":"Howard","year":"2013","journal-title":"Proc. Inst. Acoust."},{"key":"ref_17","first-page":"204","article-title":"Remote Beehive Monitoring using Acoustic Signals","volume":"42","author":"Qandour","year":"2014","journal-title":"Acoust. Aust."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cejrowski, T., Szymanski, J., Mora, H., and Gil, D. (2018, January 19\u201321). Detection of the Bee Queen Presence Using Sound Analysis. Proceedings of the Asian Conference on Intelligent Information and Database Systems, Dong Hoi City, Vietnam.","DOI":"10.1007\/978-3-319-75420-8_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kulyukin, V., Mukherjee, S., and Amlathe, P. (2018). Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples. Appl. Sci., 8.","DOI":"10.3390\/app8091573"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H.L., and Benetos, E. (2019, January 12\u201317). Audio-based identification of beehive states. Proceedings of the ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682981"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gil-Lebrero, S., Quiles-Latorre, F.J., Ortiz-L\u00f3pez, M., S\u00e1nchez-Ruiz, V., G\u00e1miz-L\u00f3pez, V., and Luna-Rodr\u00edguez, J.J. (2017). Honey Bee Colonies Remote Monitoring System. Sensors, 17.","DOI":"10.3390\/s17010055"},{"key":"ref_22","unstructured":"Zacepins, A., Stalidzans, E., and Meitalovs, J. (2012, January 15\u201318). Application of information technologies in precision apiculture. Proceedings of the 13th International Conference on Precision Agriculture ICPA 2012, Indianapolis, IN, USA."},{"key":"ref_23","unstructured":"Zacepins, A., and Karasha, T. (2013, January 23\u201324). Application of temperature measurements for the bee colony monitoring: A review. Proceedings of the 12th International Scientific Conference \u201cEngineering for Rural Development\u201d, Jelgava, Latvia."},{"key":"ref_24","unstructured":"(2019, October 29). Open Source Beehives Project. Available online: https:\/\/www.osbeehives.com\/pages\/about-us."},{"key":"ref_25","unstructured":"(2012). Standard IEEE 802.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications\u2033, IEEE Press. 2012 revision, IEEE-SA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1561\/2000000004","article-title":"The application of hidden Markov models in speech recognition","volume":"1","author":"Gales","year":"2007","journal-title":"Found. Trends Signal Process."},{"key":"ref_27","first-page":"377","article-title":"On Automatic Speech Recognition at the Dawn of the 21st Century","volume":"86","author":"Lee","year":"2003","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/BF02943243","article-title":"Comparison of Different Implementations of MFCC","volume":"16","author":"Zheng","year":"2001","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1023\/A:1023410018862","article-title":"Efficient noise robust feature extraction algorithms for distributed speech recognition (DSR) systems","volume":"6","author":"Kotnik","year":"2003","journal-title":"Int. J. Speech Technol."},{"key":"ref_30","unstructured":"(2019, December 18). Bee Classification Approach. Available online: https:\/\/github.com\/dsplabbee\/dspbee."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Terenzi, A., Cecchi, S., Orcioni, S., and Piazza, F. (2019, January 23\u201325). Features Extraction Applied to the Analysis of the Sounds Emitted by Honey Bees in a Beehive. Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia.","DOI":"10.1109\/ISPA.2019.8868934"},{"key":"ref_32","unstructured":"Nolasco, I., and Benetos, E. (2018). To bee or not to bee: Investigating machine learning approaches for beehive sound recognition. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:43:35Z","timestamp":1760190215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,19]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010021"],"URL":"https:\/\/doi.org\/10.3390\/s20010021","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,19]]}}}