Search Results (64)

Beekeeping is a profitable and mind-relaxing practice; however, monitoring beehives poses significant challenges, such as consuming time and potentially disturbing hive equilibrium, which may lead to colony collapse. Developing precision beekeeping (PB) systems is crucial to assist beekeepers in decision-making, automate redundant hive maintenance, and enhance the security and comfort of bee life. This review systematically explores research on PB systems, based on a keyword-driven search of Scopus and Web of Science databases, yielding 46 relevant publications. The analysis highlights a notable increase in research activity in the field since 2016. The integration of advanced technologies, including machine learning, cloud computing, IoT, and scenario-based communication methods, has proven instrumental in predicting hive states such as queen status, enemy attacks, readiness for harvest, swarming events, and population decline. Commonly measured parameters include hive weight, temperature, and relative humidity, with various sensors employed to ensure precision while minimizing bee disturbance. Additionally, bee traffic monitoring has emerged as a critical approach to assessing hive health. Most studies focus on honeybees rather than stingless bees and, in the context of enemy identification, Varroa destructor is the primary target. This review underscores the potential of novel technologies to revolutionize apiculture and enhance hive management practices. Full article

Honey bees are very important to the ecological environment and human society, contributing significantly to biodiversity and global food security, with an estimated annual impact of $15 billion in crop pollination in the USA. Over 62% of honey bee colony decline has been observed between June 2024 and February 2025. This study investigates bee stress level monitoring due to external disturbances like mechanical vibrations by measuring internal air temperature, relative humidity, and CO₂ gas concentration levels of beehives. A new wireless sensor board for real-time monitoring of honey bee colonies was designed, built, and validated. The board incorporates NDIR-based SCD30 and SCD41 sensors for CO₂, temperature, and humidity monitoring, integrated with a custom-designed two-layer printed circuit board and a Particle Argon^TM microprocessor for Wi-Fi communication. The developed board was tested and validated with live beehives in summer and winter of 2024 and 2025. The experimental study results showed the adequacy of the built sensor board. Bee colony responses on the applied stimuli (knocks) show that bees responded with a temperature increase of over 5 °C, CO₂ concentration increase by 3000 to over 10,000 ppm, and, at the same time, relative humidity drop by about 10% inside beehives. Full article

Honey bees are essential both for many global ecosystems and apicultural production. The management of bee colonies remains labour-intensive, which drives a need for automated solutions. This work presents a proof-of-concept system to monitor honey bee activity by identifying the yearly lifecycle stages exhibited by the colony. A non-invasive vibration monitoring system was developed and placed on top of brood frames in Warsaw-type beehives to collect vibration data over a full apicultural season. The recorded vibration signals were analyzed using both Convolutional Neural Networks (CNNs) and classical machine learning approaches such as the extra trees method. Recursive Feature Elimination with Cross-Validation (RFECV) was performed to isolate the most important frequency bins for lifecycle period identification. The results demonstrate that the critical frequencies for recognizing yearly honey bee activity are concentrated below 1 kHz. The proposed machine learning models achieved a weighted accuracy score of over 95%. These findings have significant implications for future bee monitoring hardware design, indicating that sampling frequencies may be reduced to as low as 2 kHz without significantly compromising model accuracy. 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

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. 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

Over the past years, increasing attention has been drawn to the adverse effects of agricultural pesticide use on pollinators, with honeybees being especially vulnerable. The aim of this study was to evaluate the levels of residues detectable and/or quantifiable of neonicotinoid pesticides and other pesticides in biological materials (bees, bee brood, etc.) and beehive products (honey, pollen, etc.) applied as seed dressings in rapeseed and sunflower plants in two growing seasons (2020–2021) in fields located in three agro-climatic regions in Romania. The study involved the comparative sampling of hive products (honey, pollen, adult bees, and brood) from experimental and control apiaries, followed by pesticide residue analysis in an accredited laboratory (Primoris) using validated chromatographic techniques (LC-MS/MS and GC-MS). Toxicological analyses of 96 samples, including bees, bee brood, honey, and pollen, confirmed the presence of residues in 46 samples, including 10 bee samples, 10 bee brood samples, 18 honey samples, and 8 pollen bread samples. The mean pesticide residue concentrations detected in hive products were 0.032 mg/kg in honey, 0.061 mg/kg in pollen, 0.167 mg/kg in bees, and 0.371 mg/kg in bee brood. The results highlight the exposure of honeybee colonies to multiple sources of pesticide residue contamination, under conditions where legal recommendations for the controlled application of agricultural treatments are not followed. The study provides relevant evidence for strengthening the risk assessment framework and underscores the need for adopting stricter monitoring and regulatory measures to ensure the protection of honeybee colony health. Full article

Thermoregulatory fanning behavior in honeybees is a vital indicator of colony health and environmental response. This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. Three state-of-the-art single-shot object detection models (YOLOv8, YOLO11, YOLO12) are evaluated using standard RGB input and two motion-enhanced encodings: Temporally Stacked Grayscale (TSG) and Temporally Encoded Motion (TEM). Results show that models incorporating temporal information via TSG and TEM significantly outperform RGB-only input, achieving up to 85% mAP@50 with real-time inference capability on high-performance GPUs. Deployment tests on the Jetson AGX Orin platform demonstrate feasibility for edge computing, though with accuracy–speed trade-offs in smaller models. This work advances real-time, non-invasive monitoring of hive health, with implications for precision apiculture and automated behavioral analysis. 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

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested. Full article

We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does seasonality matter in the ARIMA forecasting of hive weight and in-hive temperature? (b) To what extent do the best forecasters of one hive generalize to other hives? and (c) Which time series type (i.e., hive weight or in-hive temperature) is better predictable? Our principal findings were as follows: (1) The hive weight and in-hive temperature series were not white noise, were not normally distributed, and, for most hives, were not difference- or trend-stationary; (2) Seasonality matters, in that seasonal ARIMA (SARIMA) forecasters outperformed their ARIMA counterparts on the curated dataset; (3) The best hive weight and in-hive temperature forecasters of the ten monitored colonies appeared to be colony-specific; (4) The accuracy of the hive weight forecasts was consistently higher than that of the in-hive temperature forecasts; (5) The weight and temperature forecasts exhibited common qualitative patterns. 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

This study investigates the impact of Nosema infection in beehives on the physico-chemical and biochemical properties and spectral characteristics of honey as indicators of honey quality. Comprehensive analyses were performed on honey samples from hives with varying levels of Nosema infection, examining water content, free acidity, optical rotation, electrical conductivity, sugar composition, catalase activity, and pollen content. Honey from highly infected hives showed higher water content (up to 17.3%), lower optical rotation, reduced electrical conductivity, decreased glucose levels, and increased sucrose levels. Principal component analysis (PCA) identified distinct clustering of samples based on infection levels, with changes in the sugar profile, particularly higher phenolic compounds, correlating with increased infection levels. Fluorescence spectroscopy combined with PARAFAC modeling identified proteins and phenolic compounds as key discriminators of honey from infected hives. Correlation and PLS modeling further demonstrated strong relationships between spectral features and honey properties, including catalase activity and pollen content. This research presents a novel approach to evaluating the impact of Nosema infection on honey quality by integrating physico-chemical and biochemical analyses and sugar composition profiling with advanced spectroscopic techniques. These insights are invaluable for improving bee health monitoring practices and advancing sustainability in the beekeeping and honey production industries. Full article

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R² ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis. Full article