[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120733-en":3,"doc-seo-120733-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120733,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning and Computer Vision Techniques in Bee Monitoring Applications - Overview and Automated Bee Counting Demonstration","Machine learning and computer vision enable solutions to complex tasks and offer practical value for monitoring honeybee colonies and assessing health conditions. Automated methods can identify dangerous states earlier, support more effective scheduling of inspections, and reduce operational costs for bee colony research. This paper surveys state-of-the-art computer vision and machine learning approaches for bee monitoring, and illustrates their use through an example automated bee counter. The structure includes brief theoretical introductions so veterinary and apidology professionals can apply these techniques.","Machine Learning and Computer Vision Techniques in Bee Monitoring Applications  \nSimon Bilik: [bilik@vut.cz](bilik@vut.cz1)[1](bilik@vut.cz1), Ondrej Bostik: [bostik@vut.cz](bostik@vut.cz), Lukas Kratochvila: [kratochvila@vut.cz](kratochvila@vut.cz), Adam Ligocki: [ligocki@vut.cz](ligocki@vut.cz), Matej Poncak: [203325@vut.cz](203325@vut.cz), Tomas Zemcik: [zemcikt@vut.cz](zemcikt@vut.cz), Milos Richter: [richter@vut.cz](richter@vut.cz), Ilona Janakova: [janakova@vut.cz](janakova@vut.cz), [Petr](Petr Honec: honecp@vut.cz)[ Honec](Petr Honec: honecp@vut.cz)[: ](Petr Honec: honecp@vut.cz)[honecp@vut.cz](Petr Honec: honecp@vut.cz), [Karel](Karel Horak: horak@vut.cz)[ Horak](Karel Horak: horak@vut.cz)[: horak@vut.cz](Karel Horak: horak@vut.cz)  \nAbstract: Machine learning and computer vision are dynamically growing fields, which have proven to be able to solve very complex tasks. They could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. In this paper, we present an overview of the state-of-the-art computer vision and machine learning applications used for bee monitoring. We also demonstrate the potential of those methods as an example of an automated bee counter algorithm. The paper is aimed at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce to them its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use the machine learning techniques for other applications in bee monitoring.  \nKeywords: Bee monitoring, Bee inspection, Machine Learning, Deep Learning, Computer Vision, Object detection, Varroasis detection, Bee counting  \n1. Introduction  \nHoneybee (Apis mellifera) is the most important pollinator worldwide and it covers a significant part of the floral visits, some of them exclusively [1] . Nevertheless, bee colonies worldwide face many problems connected with the parasites, diseases, and colony collapse, which might cause problems with the food supply chains and huge economic losses. This problem affects both wild and breeding pollinators with the expected reduction from 3 to 8% of the agricultural production in the case of the total absence of animal pollinators [2] .  \nIn recent years, modern machine learning techniques have proven to be very efficient for processing multidimensional data with a huge amount of information and dependencies. These techniques which are often used in computer vision applications made a breakthrough in previously hard-to-solve problems, such as classification, recognition, or inspection tasks, and they could be successfully applied also to the automated bee inspection methods. An exhaustive overview of those methods developed over the last century is presented in [3], but despite the high quality of this paper, the machine learning-based methods are mentioned only briefly. We aim to cover the existing gap with this paper, because a wider use of these techniques could  \n1 Brno University of Technology  \nlead to time and economic savings in bee colony research, or to early recognition of the potentially dangerous situations, possibly allowing for a corrective action before dramatic action is required. Most of the described techniques could be easily used for the recognition of the pollen-bearing bees, bee counting, foreign insect detection, or the early diagnosis of various infections.  \nFor the above-mentioned reasons, this paper presents the state-of-the-art methods for bee monitoring based on machine learning, computer vision or their combination in more detail. In the first part of the paper, we briefly describe the basic concepts of machine learning, its prere","cbCaipM8vfbOXcas","https://ap.wps.com/l/cbCaipM8vfbOXcas","pdf",1262926,1,19,"English","en",105,"# Introduction\n# Bee monitoring and artificial intelligence\n## Machine learning foundations\n## Supervised vs. unsupervised learning","[{\"question\":\"How can machine learning and computer vision improve honeybee monitoring?\",\"answer\":\"They can analyze complex multidimensional data to support automated bee inspection and health-state identification. Early detection of potentially dangerous situations can enable corrective action before conditions become critical.\"},{\"question\":\"Who is the intended audience for this paper?\",\"answer\":\"The paper is aimed at veterinary and apidology professionals and experts. It includes brief theoretical introductions to help readers who may not be familiar with machine learning understand its possibilities.\"},{\"question\":\"What demonstration does the paper include to illustrate practical use?\",\"answer\":\"It demonstrates the potential of these methods via an example automated bee counter algorithm. The goal is to show how computer vision and learning techniques can be applied in bee counting tasks.\"}]","Machine Learning and Computer Vision Techniques in Bee Monitoring Applications - Overview and Automated Bee Counting Demonstration | PDF",1785731755,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-computer-vision-techniques-in-bee-monitoring-applications-overview-and-automated-bee-counting-demonstration","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-computer-vision-techniques-in-bee-monitoring-applications-overview-and-automated-bee-counting-demonstration/120733/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How can machine learning and computer vision improve honeybee monitoring?","Question",{"text":75,"@type":76},"They can analyze complex multidimensional data to support automated bee inspection and health-state identification. Early detection of potentially dangerous situations can enable corrective action before conditions become critical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Who is the intended audience for this paper?",{"text":80,"@type":76},"The paper is aimed at veterinary and apidology professionals and experts. It includes brief theoretical introductions to help readers who may not be familiar with machine learning understand its possibilities.",{"name":82,"@type":73,"acceptedAnswer":83},"What demonstration does the paper include to illustrate practical use?",{"text":84,"@type":76},"It demonstrates the potential of these methods via an example automated bee counter algorithm. 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