{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:45:38Z","timestamp":1785415538014,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2318597"],"award-info":[{"award-number":["2318597"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2030188"],"award-info":[{"award-number":["2030188"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2318597"],"award-info":[{"award-number":["2318597"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"publisher"}]},{"award":["2030188"],"award-info":[{"award-number":["2030188"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Puerto Rico at Rio Piedras"},{"id":[{"id":"https:\/\/ror.org\/0453v4r20","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Bees are essential pollinators for agricultural systems, making accurate, automated monitoring of their behavior critical for assessing colony health and ecosystem stability. Recent advances in computer vision and artificial intelligence have enabled large-scale bee traffic monitoring at hive entrances; however, most existing event classification methods focus exclusively on simple entrance and exit events. This simplification overlooks compound movements\u2014such as U-turns and guarding behaviors\u2014that represent a substantial portion of bee activity and can lead to inaccurate trajectory reconstruction and misleading behavioral interpretations. In this work, we systematically analyze existing event classification strategies used in automatic bee traffic monitoring, evaluating their performance on both simple and compound movements. We then propose extended classification methods that explicitly model compound events by incorporating bidirectional movement patterns derived from positional and angular cues. Using a manually annotated dataset of computer-vision-based hive entrance recordings, we compare threshold-based, displacement-based, and angle-based approaches under simple and mixed-event conditions. Our results demonstrate that compound events account for over one-third of all detected movements and that classification methods explicitly designed to handle bidirectional behavior substantially outperform traditional approaches in both accuracy and robustness. In particular, threshold-based bidirectional classification achieves near-perfect performance when full trajectories are available, while displacement-based methods provide a reliable alternative under partial observations. These findings highlight the importance of modeling compound behaviors in automated bee monitoring systems and contribute to more accurate flight reconstruction, behavioral analysis, and AI-driven decision support for precision agriculture and pollinator management.<\/jats:p>","DOI":"10.3390\/informatics13050065","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T09:09:12Z","timestamp":1776935352000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Relevance of Compound Events in Bee Traffic Monitoring"],"prefix":"10.3390","volume":"13","author":[{"given":"Andrea","family":"Nieves-Rivera","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Puerto Rico at R\u00edo Piedras, San Juan, PR 00925, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-0786","authenticated-orcid":false,"given":"Marie","family":"Lluberes-Contreras","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Puerto Rico at R\u00edo Piedras, San Juan, PR 00925, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0127-5885","authenticated-orcid":false,"given":"R\u00e9mi","family":"M\u00e9gret","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Puerto Rico at R\u00edo Piedras, San Juan, PR 00925, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1126\/science.1230200","article-title":"Wild pollinators enhance fruit set of crops regardless of honey bee abundance","volume":"339","author":"Garibaldi","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kulyukin, V.A., Coster, D., Kulyukin, A.V., Meikle, W., and Weiss, M. 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