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Information and communication technologies are part of almost any branch of human lives. During the last decade also beekeeping joined the direction of application of IT tools and solutions and precision beekeeping was defined. Still in the beekeeping many operations and observations are completed manually, and there is a potential to switch to the digital realisation. Information technologies can be used in the beekeeping to partly support the beekeepers by implementation of automatic or semi-automatic solutions for bee colony remote monitoring, apiary record making and other actions. The aim of this paper is to make a summary of the usage of information technology tools by the beekeepers in Latvia, summarizing precision beekeeping development status and conclude about its future development potential. To achieve this aim, in cooperation with Latvian Beekeepers Association, a beekeepers survey was conducted. More than 200 beekeepers shared thoughts and opinions about their application of information and communication technologies for monitoring the bee colonies and apiary management. The summary of the survey conducted is described in this study.

Honey bees are key insect pollinators, providing important economic and ecological value for human beings and ecosystems. This has triggered the development of several monitoring methods for assessing the temporal development of colony size, food storage, brood and pathogens. Nonetheless, most of these methods are based on visual assessments that are observer-dependent and prone to bias. Furthermore, the impact on colony development (invasiveness), as well as accuracy, were rarely considered when implementing new methods. In this study, we present and test a novel accurate and observer-independent method for honey bee colony assessment, capable of being fully standardized. Honey bee colony size is quantified by assessing the weight of adult bees, while brood and provision are assessed by taking photos and conducting image analysis of the combs with the image analysis software Deepbee®. The invasiveness and accuracy of the method were investigated using field data from two experimental apiaries in Portugal, comparing results from test and control colonies. At the end of each field experiment, most of the tested colonies had the same colony size, brood levels and honey production as the control colonies. Nonetheless, continuous weight data indicated some disturbance in tested colonies in the first year of monitoring. The overall accuracy of the image analysis software was improved by training, indicating that it is possible to adapt the software to local conditions. We conclude that the use of this fully quantitative method offers a more accurate alternative to classic visual colony assessments, with negligible impact on colony development. Honey bees are key insect pollinators, providing important economic and ecological valuefor human beings and ecosystems. This has triggered the development of several monitoringmethods for assessing the temporal development of colony size, food storage, broodand pathogens. Nonetheless, most of these methods are based on visual assessments thatare observer-dependent and prone to bias. Furthermore, the impact on colony development(invasiveness), as well as accuracy, were rarely considered when implementing new methods.In this study, we present and test a novel accurate and observer-independent method forhoney bee colony assessment, capable of being fully standardized. Honey bee colony size isquantified by assessing the weight of adult bees, while brood and provision are assessed bytaking photos and conducting image analysis of the combs with the image analysis softwareDeepbeeVR . The invasiveness and accuracy of the method were investigated using field datafrom two experimental apiaries in Portugal, comparing results from test and control colonies.At the end of each field experiment, most of the tested colonies had the same colony size,brood levels and honey production as the control colonies. Nonetheless, continuous weightdata indicated some disturbance in tested colonies in the first year of monitoring. The overallaccuracy of the image analysis software was improved by training, indicating that it ispossible to adapt the software to local conditions. We conclude that the use of this fullyquantitative method offers a more accurate alternative to classic visual colony assessments,with negligible impact on colony development.