Search Results (40)

Honey variety authentication is critical for ensuring market integrity and protecting consumer rights, especially for high-value unifloral honeys, such as acacia honey, which are frequently adulterated with low-value alternatives such as rape honey due to their similar visual appearance. The aim of this study was to develop a method for precise discrimination between rape honey and acacia honey using their chemical profiles combined with machine learning. A total of 542 honey samples were collected from major beekeeping regions in China. Targeted quantification of 12 sugars and 20 amino acids was performed using UPLC-MS/MS. Multivariate analysis revealed significant differences in sugar and amino acid compositions between the two honey types, though partial samples overlapped due to chemical similarity. Six machine learning algorithms, including the Multilayer Perceptron, were employed for classification. Optimization was performed via 10-fold cross-validation and ADASYN oversampling, yielding optimal performance of 98% and 100% prediction accuracies for rape honey and acacia honey, respectively, on the independent test set. SHAP (Shapley Additive Explanations) analysis identified key differential markers, including fructose, turanose, glucose, and GABA, which contributed most to the classification. Furthermore, a user-friendly web application was developed to facilitate rapid on-site authentication. This study provides an innovative technical framework for honey variety discrimination, with potential applications in quality control and anti-fraud practices. Full article

Honeybee products (HBPs), including honey, bee pollen, bee bread, royal jelly, propolis, beeswax, and bee brood, are increasingly used in food, nutraceutical, and cosmetic contexts. Because of their natural origin, HBPs can provoke allergic reactions ranging from localised dermatitis to life-threatening, systemic anaphylaxis. As the use of bee products for health purposes grows in apitherapy (a branch of alternative medicine), raising public awareness of their potential risks is essential. This narrative review synthesises the clinical manifestations of HBP allergy, culprit allergens present in each product, immunological mechanisms, diagnostic approaches, at-risk populations, and knowledge gaps. The analysis of the available literature suggests that, although relatively rarely, HPB may trigger allergic reactions, including anaphylactic shock. The sensitisation mechanism may be associated with both primary sensitisation and cross-reactivity and can be classified into type I (IgE-mediated) and type IV (T-cell-mediated). However, bee bread appears less allergenic than other HBPs, potentially due to lactic fermentation that can degrade allergenic proteins. Severe reactions following intake of bee bread have not been reported to date. Management of HBP allergic reactions centres on avoiding the products, educating about the risks, and providing more precise product labelling, specifying the allergen content. Individuals with atopy and beekeepers are at heightened risk of developing anaphylaxis; therefore, they should be particularly aware of the potential dangerous consequences of HPB use. Further research is needed to clarify the mechanisms of HBP allergies and improve safety for all users. Full article

Trophallaxis, a fundamental social behavior observed among honeybees, involves the redistribution of food and chemical signals. The automation of its detection under field-realistic conditions poses a significant challenge due to the presence of crowding, occlusions, and brief, fine-scale motions. In this study, we propose a markerless, deep learning-based approach that injects short- and mid-range temporal features into single-frame You Only Look Once (YOLO) detectors via temporal-to-RGB encodings. A new dataset for trophallaxis detection, captured under diverse illumination and density conditions, has been released. On an NVIDIA RTX 4080 graphics processing unit (GPU), temporal-to-RGB inputs consistently outperformed RGB-only baselines across YOLO families. The YOLOv8m model improved from 84.7% mean average precision (mAP50) with RGB inputs to 91.9% with stacked-grayscale encoding and to 95.5% with temporally encoded motion and averaging over a 1 s window (TEMA-1s). Similar improvements were observed for larger models, with best mAP50 values approaching 94–95%. On an NVIDIA Jetson AGX Orin embedded platform, TensorRT-optimized YOLO models sustained real-time throughput, reaching 30 frames per second (fps) for small and 23–25 fps for medium models with temporal-to-RGB inputs. The results showed that the TEMA-1s encoded YOLOv8m model has achieved the highest mAP50 of 95.5% with real-time inference on both workstation and edge hardware. These findings indicate that temporal-to-RGB encodings provide an accurate and computationally efficient solution for markerless trophallaxis detection in field-realistic conditions. This approach can be further extended to multi-behavior recognition or integration of additional sensing modalities in precision beekeeping. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Apiculture is among the most weather-dependent sectors of agriculture; however, quantifying the impact of meteorological factors remains challenging. Beehive weight has long been recognized as an important indicator of colony health, strength, and food availability, as well as foraging activity. Atmospheric influences on hive weight dynamics have been a subject of research since the early 20th century. This study aims to estimate hourly hive weight variation by applying linear time-series models to hive weight data collected from active apiaries during intensive foraging periods, considering atmospheric predictors. We employed a rolling 24 h forward ARIMAX and SARIMAX model, incorporating meteorological variables as exogenous factors. The median estimates for the study period resulted in model RMSE values of 0.1 and 0.3 kg/h. From numerous meteorological variables, the hourly maximum temperature was found to be the most significant predictor. ARIMAX model results also exhibited a strong diurnal cycle, pointing out the weather-driven seasonality of hive weight variations. Full article

Traditional methods for assessing honey storage in beehives predominantly rely on manual visual inspection, which often leads to inconsistencies and inefficiencies. This study presents an automated deep learning approach utilizing the YOLOv11 model to detect, classify, and quantify honey cells within Apis mellifera frames across monthly sampling periods. The model’s performance varied depending on image resolution and dataset partitioning. Using the free version of YOLOv11 with high-resolution images (960 × 960 resolution) and a dataset split of 90:5:5 for training, validating, and testing, the model achieved a mean average precision at IoU threshold of 0.5 (mAP@0.5) of 83.4% for uncapped honey cells and 80.5% for capped honey cells. A strong correlation (r = 0.94) was observed between the 90:5:5 and 80:10:10 dataset splits, indicating that increasing the volume of training data enhances classification accuracy. In parallel, the study investigated the relationship between the physical properties of honey and image-based honey storage detection. Of the four tested properties, electrical conductivity (R² = 0.19) and color (R² = 0.21) showed weak predictive power for honey storage area estimation, with even weaker associations found for pH and moisture content. The honey storage areas via 90:5:5 and 80:10:10 datasets moderately correlated (r = 0.44–0.46) with increasing electrical conductivity and color. Especially, electrical conductivity exhibited statistically significant correlations with dataset performance across different dataset splits (p < 0.05), suggesting some potential influence of chemical composition on model accuracy. Our findings demonstrate the viability of image-based honey classification as a reliable technique for monitoring beehive productivity. Additionally, the research on image-based honey detection can be a non-invasive solution for improved honey production, beehive productivity, and optimized beekeeping practices. 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

Bees are crucial for food production and biodiversity. However, extreme weather variation and harsh winters are the leading causes of colony losses and low honey yields. This study aimed to identify the most important features and predict Total Honey Harvest (THH) by combining machine learning (ML) methods with climatic conditions and environmental factors recorded from the winter before and during the harvest season. The initial dataset included 598 THH records collected from five apiaries in Lombardy (Italy) during spring and summer from 2015 to 2019. Colonies were classified into medium-low or high production using the 75th percentile as a threshold. A total of 38 features related to temperature, humidity, precipitation, pressure, wind, and enhanced vegetation index–EVI were used. Three ML models were trained: Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost). Model performance was evaluated using accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC). All models reached a prediction accuracy greater than 0.75 both in the training and in the testing sets. Results indicate that winter climatic conditions are important predictors of THH. Understanding the impact of climate can help beekeepers in developing strategies to prevent colony decline and low production. 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

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS. Full article

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in $1024\times 576$ px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on $640\times 384$ px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications. Full article

Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. However, developing a monitoring system using CNNs compared to conventional machine learning models can result in higher computation costs, greater energy demand, and longer inference times. This study examines the potential of CNN architectures in developing a monitoring system based on constrained hardware. The experimentation involved testing ten CNN architectures from the PyTorch and Torchvision libraries on single-board computers: an Nvidia Jetson Nano (NJN), a Raspberry Pi 5 (RPi5), and an Orange Pi 5 (OPi5). The CNN architectures were trained using four datasets containing spectrograms of acoustic samples of different durations (30, 10, 5, or 1 s) to analyze their impact on performance. The hyperparameter search was conducted using the Optuna framework, and the CNN models were validated using k-fold cross-validation. The inference time and power consumption were measured to compare the performance of the CNN models and the SBCs. The aim is to provide a basis for developing a monitoring system for precision applications in apiculture based on constrained devices and CNNs. Full article

Natural products of bee origin, despite their complex composition and difficulties in standardization, have been of high interest among scientists representing various disciplines from basic sciences to industrial and practical implementation. As long as their use is monitored and they do not impact human health, they can be considered valuable sources of many chemical compounds and are potentially useful in medicine, food processing, nutrition, etc. However, apart from honey, the general turnover of bee products lacks precise and detailed legal requirements ensuring their quality. The different residues in these products constitute a problem, which has been reported in numerous studies. All products derived from beekeeping are made by bees, but they are also influenced by the environment. Such a dual pathway requires detailed surveillance of hazards stemming from outside and inside the apiary. This should be ensured via harmonized requirements arising from the binding legal acts, especially in international and intercontinental trade zones. Full article