Search Results (90)

Honey bees are vital to ecosystem conservation, agricultural production, and biodiversity, yet their welfare has often been overlooked. This study introduces the integration of Honey Bee Welfare Practices (HBWPs) into the One Welfare framework, addressing the interconnectedness of honey bee welfare, environmental welfare, and human wellbeing. We analysed and re-evaluated the 243 HBWPs already identified and categorised within the context of the Five Domains model in 2024 by Giovanni Formato et al., and we explored their broader impacts. By incorporating the One Welfare approach, we assessed each practice’s effect on bee welfare both as individuals and as a superorganism, human wellbeing, and environmental welfare, as well as their economic and time-related implications for beekeepers. The aim of this study was to obtain a list of One Welfare Practices in Beekeeping, considering all stakeholders as equally important. The analysis highlights the multidimensional nature of beekeeping, with 280 practices positively affecting honey bee welfare, while also considering their potential impact on human wellbeing, environmental health, and production. Challenges such as balancing beekeeper time constraints and welfare goals are discussed, with recommendations for practical compromises. This approach can offer a holistic and sustainable model for apiculture, ensuring that welfare is maintained across all stakeholders, and provides a flexible framework applicable to various beekeeping systems worldwide. Full article

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

The growing recognition of animal sentience has led to notable progress in European Union animal welfare legislation. However, a significant inconsistency remains: while mammals, birds, and cephalopods are legally protected as sentient beings, honeybees (Apis mellifera)—despite robust scientific evidence of their cognitive, emotional, and sensory complexity—are excluded from such protections. This manuscript examines, from an interdisciplinary perspective, the divergence between emerging evidence on invertebrate sentience and current EU legal frameworks. Honeybees and cephalopods serve as comparative case studies to assess inconsistencies in the criteria for legal recognition of sentience. Findings increasingly confirm that honeybees exhibit advanced cognitive functions, emotional states, and behavioral flexibility comparable to those of legally protected vertebrates. Their omission from welfare legislation lacks scientific justification and raises ethical and ecological concerns, especially given their central role in pollination and ecosystem stability. In general, we advocate for the inclusion of Apis mellifera in EU animal welfare policy. However, we are aware that there are also critical views on their introduction, which we address in a dedicated paragraph of the manuscript. For this reason, we advocate a gradual and evidence-based approach, guided by a permanent observatory, which could ensure that legislation evolves in parallel with scientific understanding, promoting ethical consistency, sustainable agriculture, and integrated health under the One Health framework. This approach would meet the concerns of consumers who consider well-being and respect for the environment as essential principles of breeding, and who carefully choose products from animals raised with systems that respect welfare, with indisputable economic advantages for the beekeeper. Full article

Bee pollen’s aroma combined with other floral components serve various purposes, including attracting pollinators and signaling the availability of food sources. The present study aimed to comparatively analyze the volatile profiles of unifloral pollen taxa. Bee pollen loads were collected using pollen traps and sorted based on their botanical origin, determined by color and pollen grain morphology. The separated pollen samples were analyzed using a Purge & Trap/GC-MS system, identifying the volatile profiles of pollen from 16 plant species. The analysis revealed distinguished differences in the total volatile organic compounds (VOCs) among the various pollen species. Notably, the pollen from Erica manipuliflora, Papaver rhoeas, and Sisymbrium irio contained the highest number of VOCs, with 54, 51, and 42 substances detected, respectively. Certain volatile compounds appeared to correlate with increased bee visitation. For instance, 4-methyl-5-nonanone was uniquely found in E. manipuliflora pollen, while isothiocyanate compounds were exclusively present in species of the Brassicaceae family. Therefore, given the significant impact of VOCs on honey bees’ preferences, it is essential to consider not only the nutritional value of bee pollen when evaluating its beekeeping value, but also its aroma profile. Full article

Beekeeping is facing a serious crisis due to climate change and diseases such as bee mites (Varroa destructor), which have led to declining populations, collapsing colonies, and reduced beekeeping productivity. Bee mites are small, reddish-brown in color, and difficult to distinguish from bees. Rapid bee mite detection techniques are essential for overcoming this crisis. This study developed a technology for recognizing bee mites and beekeeping objects in beecombs using the You Only Look Once (YOLO) object detection algorithm. The dataset was constructed by acquiring RGB images of beecombs containing mites. Regions of interest with a size of 640 × 640 pixels centered on the bee mites were extracted and labeled as seven classes: bee mites, bees, mite-infected bees, larvae, abnormal larvae, and cells. Image processing, data augmentation, and stratified data distribution methods were applied to enhance the object recognition performance. Four datasets were constructed using different augmentation and distribution strategies, including random and stratified sampling. The datasets were partitioned into training, testing, and validation sets in a 7:2:1 ratio, respectively. A YOLO-based model for the detection of bee mites and seven beekeeping-related objects was developed for each dataset. The F1 scores for the detection of bee mites and seven beekeeping-related objectives using the YOLO model based on original datasets were 94.1% and 91.9%, respectively. The model applied data augmentation, and stratified sampling achieved the highest performance, with F1 scores of 97.4% and 96.4% for the detection of bee mites and seven beekeeping-related objects, respectively. The results underscore the efficacy of using the YOLO architecture on RGB images of beecombs for simultaneously detecting bee mites and various beekeeping-related objects. This advanced mite detection method is expected to contribute significantly to the early identification of pests and disease outbreaks, offering a valuable tool for enhancing beekeeping practices. Full article

The last few years have seen the introduction of new technologies in beekeeping, including RFID. Using readers and miniaturized tags, RFID systems work in the UHF frequency band, allowing reading distances to reach tens of centimeters. This work analyzes the propagation inside a full beehive, composed of 10 frames supported by a wooden structure. Each frame contains a layer of beeswax supported by metallic wires. The methodology employed involves measuring Received Signal Strength Indicator (RSSI) values and simulating the environment using CST Studio. The results show that tags located along the frame’s wires have more coverage than tags in the center positions, revealing coupling of the microtag antenna with the wire. Furthermore, obtaining coverage through simulations represents a more restrictive approach than through measurements. Frame selectivity is also observed, as most of the coverage is found within the three frames closest to the reader antenna. This result shows that RFID systems can find application in the identification and location of the queen bee in a hive. Full article

Monitoring insect activity at hive entrances is essential for advancing precision beekeeping practices by enabling non-invasive, real-time assessment of the colony’s health and early detection of potential threats. This study evaluates deep learning models for detecting worker bees, pollen-bearing bees, drones, and wasps, comparing different YOLO-based architectures optimized for real-time inference on an RTX 4080 Super and Jetson AGX Orin. A new publicly available dataset with diverse environmental conditions was used for training and validation. Performance comparisons showed that modified YOLOv8 models achieved a better precision–speed trade-off relative to other YOLO-based architectures, enabling efficient deployment on embedded platforms. Results indicate that model modifications enhance detection accuracy while reducing inference time, particularly for small object classes such as pollen. The study explores the impact of different annotation strategies on classification performance and tracking consistency. The findings demonstrate the feasibility of deploying AI-powered hive monitoring systems on embedded platforms, with potential applications in precision beekeeping and pollination surveillance. Full article

In the context of increasing urbanisation, the question arises as to whether urban environments can provide honeybee colonies with floral resources comparable to those available in rural areas. The present study sought to evaluate the impact of land cover on nectar collection by bees in urban and rural apiaries. To this end, changes in the mass of 10 hives located in five urban–rural site pairs were monitored over two years (2021–2022) to assess nectar yield, weight loss, and the number of foraging days. The 3 km surroundings of each apiary were analysed using Sentinel-2 satellite imagery from the S2GLC-PL (National Satellite Information System 2025). The analysis identified eight distinct land cover classes: anthropogenic, agricultural, broad-leaved forest, coniferous forest, grassland, shrubs, wetlands, and water bodies. The findings revealed no statistically significant variation in the total nectar collected between urban and rural colonies (72.9 kg vs. 64.5 kg; p > 0.6). However, urban colonies exhibited a significantly higher number of foraging days (67 vs. 56). No significant correlations were identified between specific land cover types and nectar yield. Principal component analysis (PCA) and clustering revealed distinct landscape gradients, yet these did not influence nectar collection. The findings of this study indicate that diverse urban environments have the capacity to support beekeeping to a similar extent as rural areas and may even have superior conditions, provided that the continuity and diversity of nectar plants are maintained. Full article

Interactions between honeybees and the environment are often difficult to achieve, particularly when the purpose is to optimize beekeeping production. The present study proposed to monitor the space-time variations of melliferous resources potentially exploited by colonies within a foraging area in Bosnia & Herzegovina, characterized by contrasting landscapes. The combination of methods involving Geographical Information Systems, floristic monitoring, and modelling enabled honey production potential to be calculated for the entire foraging area. In particular, the location of taxa, their abundance, diversity, and phenology enabled us to determine the spatial distribution and temporal variation of production potential. Robinia pseudoacacia and Rubus sp. made a major contribution. This potential was highly contrasted, with distant areas from the apiary more attractive than closer ones, depending on the moment. Specific periods, such as June were particularly conducive to establishing a high potential. Forest and grassland played a major role in the temporal succession, mainly because of the area covered, but moments with lower potential were supported by specific land uses (orchards). Land uses with a small surface area, such as orchards, wasteland, and riparian zones had a high potential per unit area, and improving the production potential within a foraging area could involve increasing these specific surfaces. Full article

The honeybee comb serves as the primary site for all essential colony activities, and its structural and functional integrity plays a crucial role in colony development. As combs age through successive brood-rearing cycles, their physicochemical properties undergo significant changes, which can negatively affect colony health and productivity. This review synthesizes the current knowledge on the biological functions of combs, the aging process, and the negative impacts of old combs on cell structure, worker morphology, colony strength, and bee product quality. Additionally, it examines the adaptive strategies employed by honeybees and the recommended beekeeping management practices to mitigate these effects. Specifically, old combs undergo structural changes in cell dimensions and reduced spatial capacity, leading to the growth of smaller bees with diminished foraging and productivity. Furthermore, bee products, such as honey and beeswax, collected from old combs demonstrate compromised quality and higher levels of environmental contaminants. To counteract these challenges, colonies engage in hygienic behaviors, such as cell cleaning and comb rebuilding, while their enhanced immune and detoxification systems help mitigate comb-derived stressors. This review demonstrates that the systematic replacement of old brood combs is a critical management strategy to optimize bee health and ensure sustainable apiculture. Full article

The role of experimental data and the use of IoT-based monitoring systems are gaining broader significance in research on bees across several aspects: bees as global pollinators, as biosensors, and as examples of swarm intelligence. This increases the demands on monitoring systems to obtain homogeneous, continuous, and standardized experimental data, which can be used for machine learning, enabling models to be trained on new online data. However, the continuous operation of monitoring systems introduces new risks, particularly the cumulative impact of electromagnetic radiation on bees and their behavior. This highlights the need to balance IoT energy consumption, functionality, and continuous monitoring. We present a novel IoT-based bee monitoring system architecture that has been operating continuously for several years, using solar energy only. The negative impact of IoT electromagnetic fields is minimized, while ensuring homogeneous and continuous data collection. We obtained experimental data on the adverse phenomenon of honey robbing, which involves elements of swarm intelligence. We demonstrate how this phenomenon can be predicted and illustrate the interactions between bee colonies and the influence of solar radiation. The use of criteria for detecting honey robbing will help to reduce the spread of diseases and positively contribute to the sustainable development of precision beekeeping. Full article

Integrating Internet of Things (IoT) devices and machine learning (ML) techniques holds immense potential for transforming beekeeping practices. This review paper offers a critical analysis of state-of-the-art IoT-enabled precision beekeeping systems. It examines the diverse sensor technologies deployed for honeybee data acquisition, delving into their strengths and limitations, particularly regarding accuracy, reliability, energy sustainability, transmission range, feasibility, and scalability. Furthermore, this paper dissects prevalent ML models used for bee behaviour analysis, disease detection, and colony monitoring tasks. This paper evaluates their methodologies, performance metrics, and the challenges involved in selecting appropriate machine learning algorithms. It also examines the influence of sensing devices, computational complexity, dataset limitations, validation procedures, evaluation metrics, and the effects of pre-processing techniques on these models’ outcomes. Building upon this analysis, this paper identifies key research gaps and proposes promising avenues for future investigation. The focus is on the synergistic use of IoT and ML to address colony health management challenges and the overall sustainability of the beekeeping industry. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

This study aimed to determine the possibility of deploying an innovative electrical method and to establish the usefulness of conductivity and dielectric parameters for assessing the quality of Polish honeys, as well as for distinguishing their botanical origin. An attempt was also made to determine which standard physicochemical parameter could be replaced by conductivity and dielectric parameters. The experimental material consisted of seven varieties of honey (linden, rapeseed, buckwheat, goldenrod, phacelia, multifloral, acacia), obtained from beekeepers from northern Poland. Their quality was assessed based on their physicochemical parameters, biological activity, and color. Electrical parameters were measured using a measuring system consisting of an LCR meter, and own-construction sensor. Conductivity (Z, G) and dielectric (Cs, Cp) parameters were measured. Statistical analysis of the results of measurements of electrical parameters of the seven types of honey tested allowed classifying them in terms of their conductivity properties into two groups of single-flower honeys and one group of multi-flower honeys. This proves the feasibility of identifying their botanical origin using the electrical method, which is characterized by non-invasiveness, measurement speed, and high sensitivity. The usefulness of parameters Z and G in replacing quality parameters was confirmed mainly for single-flower honeys: buckwheat, linden, rapeseed, and phacelia. Full article

Background: Beekeeping plays crucial natural and economic roles but also poses health risks, as bee stings can cause severe allergic reactions like anaphylaxis, a potentially life-threatening condition that requires timely intervention. Understanding symptoms and the proper use of adrenaline autoinjectors is essential to minimize risks. This study aimed to assess the need for education on anaphylaxis and to develop a health education program to enhance beekeepers’ preparedness and safety. Methods: A qualitative descriptive interpretative method was employed. Two focus groups were conducted, one with eight health care professionals specializing in allergy and clinical immunology and the other with six active beekeepers. The data were analyzed via content analysis using QDA Miner^® Lite v3.0.5 software. Results: The analysis structure comprises five thematic areas: (1) the management of anaphylaxis; (2) the prevention of anaphylaxis; (3) health education approaches; (4) systemic approaches in prevention; and (5) adrenaline autoinjectors. The results highlight key challenges, including the need for better strategies to manage anaphylaxis, improve prevention, and provide practical educational programs for beekeepers. There is also a need for better collaboration between health care professionals and beekeepers, as well as improved access to and knowledge of adrenaline autoinjectors. Conclusions: Targeted education for beekeepers on recognizing anaphylaxis symptoms and using adrenaline autoinjectors is essential for timely intervention and preventing severe outcomes. Given their exposure to bee stings, beekeepers require proper training and regular practice to improve preparedness and safety. This research underscores the need for a comprehensive educational program to reduce anaphylaxis risk and enhance safety in beekeeping. Full article