Search Results (248)

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

Honeybee (Apis mellifera) health is governed by the integrated action of detoxification, immunity, and microbiota within complex environmental contexts. The coordinated detoxification system (DETOXome), primarily active in the midgut, fat body, and Malpighian tubules, includes cytochrome P450s, glutathione S transferases, carboxylesterases, and ABC transporters, and functions in concert with innate immune pathways such as Toll, Imd, Jak/STAT, JNK, antimicrobial peptides, and RNA interference. Cellular maintenance mechanisms, including heat shock proteins, proteostasis, and antioxidant defenses, support these systems under chemical, thermal, and pathogen-induced stress. Multi-stressor exposures encompassing pesticides, pathogens, nutritional limitations, and climate variations interact to affect physiological resilience, behavior, and colony function. This review synthesizes molecular, organ-specific, and colony-level evidence to provide a mechanistic framework connecting environmental stressors to detoxification and immune responses. Predictive markers derived from transcriptomic, proteomic, and microbiome analyses offer early detection of sublethal stress, while genomic and selective breeding strategies hold the potential to enhance honeybee resilience. By integrating stress pathways across biological scales, this review advances a unified model of honeybee health that moves beyond descriptive lists to highlight cross-system interactions driving colony survival. Full article

Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for maintaining colony viability. This study investigates the effectiveness of machine learning and deep learning models for acoustic-based queen-presence detection using short-duration hive audio recordings. Audio data collected from multiple sources were processed to extract spectrogram, Mel-spectrogram, and Mel-frequency cepstral coefficient features, which were evaluated using classical ML classifiers and convolutional neural networks. Experimental results indicate that MFCC-based representations consistently outperform spectrogram-based features across segment lengths, achieving higher accuracy and greater stability. The best performance was obtained with Mel features using convolutional neural networks for short segments and gradient-boosted models for longer windows. These findings demonstrate that brief acoustic segments are sufficient for reliable classification, supporting real-time monitoring under realistic urban recording conditions with moderate environmental noise. The proposed approach offers a scalable and low-cost framework for precision beekeeping and contributes to sustainable beekeeping through early, automated anomaly detection. The proposed framework supports real-time, low-cost deployment scenarios, enabling scalable precision apiculture solutions. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an $ϵ$-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

Nosema ceranae is a common and highly contagious fungal pathogen that primarily infects the gut of adult honeybees, causing nosemosis. As a chronic disease of the digestive system, it poses a global threat to honeybee health and colony sustainability. This study aimed to investigate the inhibitory effects of different concentrations of Scutellaria baicalensis aqueous extract on N. ceranae in the intestines of infected Apis cerana through feeding experiments. In addition, the therapeutic efficacy of its major active component, baicalin, was evaluated, and its potential molecular mechanisms of action were explored. The results showed that, compared with the control group, administration of S. baicalensis aqueous extract at concentrations of 1 mg/mL, 5 mg/mL, and 10 mg/mL significantly reduced midgut spore loads (p < 0.05). Further experiments showed that a 0.5 mg/mL baicalin sucrose solution, prepared with 0.5% (v/v) DMSO as co-solvent, exhibited optimal solubility and significantly inhibited the proliferation of spores in the honeybee midgut. Transcriptomic analysis of A. cerana revealed varying numbers of significantly differentially expressed genes among the baicalin-treated (HG) group, the co-solvent control (DMSO) group, and the blank control (C) group. Four candidate DEGs associated with the effects of baicalin were further identified, namely LOC108003965, LOC108000905, LOC107996681, and CYP4G11. Gene Ontology enrichment analysis showed that, in the comparison between the HG group and the C group, these DEGs were significantly enriched in six functional categories: iron ion binding, phosphoric ester hydrolase activity, heme binding, tetrapyrrole binding, hydrolase activity (acting on ester bonds), and oxidoreductase activity (acting on paired donors, with incorporation or reduction of molecular oxygen). Collectively, these results demonstrate that S. baicalensis aqueous extract effectively inhibits the proliferation of N. ceranae within the host, and its active component, baicalin, exhibits a similar inhibitory effect. The present study proposes a novel strategy in which baicalin may enhance host endogenous chitinase-related activity to target and disrupt the spore wall, offering a new perspective for the prevention and control of honeybee nosemosis. Full article

Ascosphaera apis is a fungal pathogen of honeybee larvae and the primary cause of chalkbrood disease, which weakens bee colonies, impairing their ability to function effectively and making them more susceptible to other pathogens and environmental stressors. This study aimed to develop and validate an in-house matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral library for A. apis. A new MALDI-TOF MS library was constructed using reference Ascosphaera species and validated through whole-genome-based confirmation of 31 clinical isolates of A. apis. Three different protein extraction methods were tested and compared: liquid cultivation, formic acid–ethanol extraction and extended direct transfer. Our findings demonstrate that MALDI-TOF MS is a rapid and reliable tool for identifying A. apis under the tested laboratory conditions and within the analyzed strain set, with no misidentifications observed for the liquid cultivation and formic acid–ethanol extraction methods. The extended direct mycelium transfer method was slightly less effective but still showed a high sensitivity of 83.9%. This study provides a foundation for improving diagnostic approaches in the management of honeybee fungal diseases. Full article

Emerging evidence indicates that non-coding RNAs (ncRNAs) play important regulatory roles in honeybee social behavior and development. However, the regulatory roles of ncRNAs in honeybees remain largely elusive. To systematically identify ncRNAs associated with queen-regulated ovary activation, we conducted whole-transcriptome sequencing on the heads of Apis mellifera workers from queenright and queenless colonies. Subsequent bioinformatics analyses were conducted to profile differentially expressed (DE) RNAs and construct potential regulatory networks. High-quality sequencing data provided a foundation for subsequent analyses. This transcriptome data yielded 3968 lncRNA transcripts, comprising 3146 known and 822 novel candidates, all of which exhibited typical structural features of lncRNAs. Comparative expression analyses revealed that 246 lncRNAs, 1439 mRNAs, and 10 miRNAs were differentially expressed. Comprehensive functional analyses indicated that the identified DElncRNAs potentially regulate sensory perception-related target mRNAs via cis-regulation, and coordinate metabolic and proteostatic reprogramming via trans-regulation to support the transition to reproductive activation in workers. Furthermore, a competing endogenous RNA network was constructed which integrated 74 DElncRNAs, 5 DEmiRNAs, and 36 DEmRNAs to predict their potential post-transcriptional interactions. Our findings highlight a comprehensive analysis of ncRNAs and mRNAs in worker heads, providing a foundation for functional validation of their roles in honeybee ovary development. Full article

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration. Full article

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

Queen bees form the core of honeybee colonies for reproduction, and their quality is the most critical factor affecting their reproductive and productive performance. In apicultural production, queen rearing requires beekeepers to perform manual larval grafting. This is strongly limited by the beekeepers’ eyesight and technical proficiency and has become a bottleneck restricting the development of modern apiculture. To overcome this long-standing technical challenge, we designed beeswax-based tools for queen rearing without grafting larvae for Apis mellifera. The tools consist of three core components: a single-sided hollow beeswax comb foundation, beeswax larval holders and beeswax queen cells with a hole at the bottom. The holders are paired with the hollows of the beeswax comb foundation and the hole of the beeswax queen cells. Following the construction of the comb by honeybees on the hollow foundation, the queen was confined to lay eggs on the single-sided comb. Subsequently, larval holders containing eggs or larvae were pulled out, assembled with beeswax queen cells, embedded in the buckles of queen-rearing frames, and placed into colonies for queen rearing. In order to verify the feasibility of the tools, a paired comparative experiment was conducted using Apis mellifera, with the tools as the treatment group and manual larval grafting as the control group. We evaluated multiple key indicators, including acceptance rate of queen cells, queen cell length at emergence, emergence rate, weight of newly emerged queen, morphological indices (thorax length/width, forewing width, hindwing length, head width), ovariole number and the relative mRNA expression of four queen development-related genes (Vg, Hex110, Hex70b, Jhamt). No significant differences were observed in queen cell acceptance rate and emergence rate between the two groups. However, compared with the control group, queens reared using the tools exhibited significantly greater queen cell length at emergence, higher emergence weight, superior morphological traits, more ovarioles and significantly upregulated expression of all four assayed genes. In conclusion, the tools can be used to rear high-quality Apis mellifera queens effectively with superior phenotypic and molecular traits compared to conventional grafting, which provides efficient and convenient queen-rearing tools for beekeepers. Full article

The extensive utilization of TMX, a substance characterized by its high toxicity towards honeybees, has exerted a deleterious influence on the employment of neonicotinoid insecticides and the proliferation of bee colonies. However, there is a lack of effective solutions to mitigate the toxicological impact of neonicotinoid insecticides on bees. The present study proposes a method of using MB to alleviate TMX poisoning in honeybee (Apis mellifera ligustica) larvae. The results demonstrated that when bee larvae ingested MB at a concentration of 0.32 mg·L⁻¹, the mortality rate of larvae could be reduced from 47.2% to 25.0%. Transcriptome analysis identified the honeybee dihydrolipoyl dehydrogenase (AmDld) gene as one of the main genes involved in the function of MB. AmDld was highly expressed in larval hemolymph. Its expression levels and enzymatic content were suppressed by either TMX or MB alone but restored by the TMX+MB combination. RNAi-mediated knockdown of AmDld decreased AmDld content and increased larval mortality under the TMX+MB co-treatment from 25.0% to 40.6%. This indicated that the TMX+MB combination rescued AmDld levels, thereby alleviating TMX toxicity to bee larvae. The present study has demonstrated that the ingestion of MB by honeybee larvae has the capacity to reduce the toxicity of TMX, a toxic substance, through the action of the AmDld gene. This provides a novel approach to mitigating pesticide poisoning in bees. Full article

Honeybee (Apis mellifera) colony density is frequently assumed to influence the level of Nosema ceranae infestation in managed colonies. In Slovakia, winter bottom beehive debris (dead worker bees) is routinely collected between January and February, providing a unique and uniform material for evaluating the degree of Nosema infestation prior to the breeding season. This study assesses the suitability of winter hive debris for estimating the infestation intensity of Nosema species and examines whether regional differences in beekeeping density are associated with variation in Nosema ceranae infestation levels. A total of 6221 samples from 43 Slovak districts collected between 2022 and 2024 were examined using microscopy confirmed by duplex PCR. Nosema ceranae was detected in 74.3% of samples, while Nosema apis was not detected. Although higher colony densities tended to be associated with increased proportions of moderately and strongly infested colonies, statistical modelling confirmed a statistically significant but modest positive association between colony density and infestation intensity. These results indicate that winter bottom beehive debris is a valuable material for assessing Nosema infestation pressure at the colony and regional levels, while also highlighting the contribution of additional environmental and management factors. Full article

We developed an intelligent acoustic health monitoring system for honeybee colonies based on wingbeat frequency analysis, offering a practical solution for modernizing apicultural practices. The system employs a three-layer architecture—the Internet of Things, fog, and cloud—to achieve real-time, non-invasive hive condition assessment. At the edge level, a Raspberry Pi and low-noise microphone continuously capture in-hive audio, which is converted into spectrograms using short-time Fourier transform (STFT). These are analyzed by a deep learning classification model deployed on the fog layer to distinguish four critical queen-related states: original queen present, queen absent, new queen rejected, and new queen accepted. The cloud layer supports data storage, visualization, and model refinement through manual annotations. Our results show that both the vision Transformer and CNN models perform effectively in classifying complex hive states, each contributing to the overall classification task, demonstrating the system’s potential for improving colony management and early intervention. This work contributes to precision apiculture by enabling scalable, real-time queen status monitoring through acoustic sensing and deep learning. Full article

Beekeeping is a widespread economic activity in rural Tanzania, supporting over 2 million livelihoods. The country’s forests and woodlands, covering approximately 55% of its land area, provide habitat for an estimated 9.2 million honeybee colonies. This positions Tanzania as the second-largest honey producer in Africa and tenth globally. Absence of current information and effective policies hinders exploitation of the industry’s potential. This review presents scientific insights into Tanzania’s beekeeping sector, focusing on honeybee species, bee products, management practices, and conservation. Among three documented subspecies of Apis mellifera (Linnaeus, 1758), A. m. scutellata is the most widespread and commonly managed by indigenous beekeepers. Tanzania annually produces over 31,000 tonnes of honey and 1800 tonnes of beeswax, generating approximately USD 77.5 million and contributing about 1% to national GDP. The industry supports livelihoods, food security, and biodiversity conservation. Its sustained growth requires effective legal and administrative support, expanded scientific research, enhanced innovation, coordinated partnerships, and integrated nationwide initiatives. Full article

Continuous human-mediated introduction of colonies and queens promotes genetic introgression and reshapes the genetic diversity and structure of local honeybee populations. According to reports, multiple non-native honeybee colonies and queens have been introduced into the DL region, leading to continuous genetic introgression. Here, we assessed the effects of continuous introgression on indigenous Apis cerana in the DL region using mtDNA and genome-wide SNP markers. We sequenced the mitochondrial tRNA ^leu-COII from 217 individuals sampled at 7 DL sites and identified 26 haplotypes defined by 18 polymorphic sites. The ΦST values indicated no internal differentiation within the Apis cerana populations in the DL region. Phylogenetic, network, ABBA-BABA test, and f3 statistic suggested introgression from both northern and southern sources. The f4-ratio indicates that approximately 16% of the ancestry in the DL group is derived from the Aba group. Genetic diversity varied widely within the DL region (Hd: 0.2907–0.8220; π: 0.0009–0.0038; K: 0.3140–1.3980), indicating different stages of introgression. The genetic structure within the DL group appears to be unstable, necessitating long-term monitoring of evolutionary processes and genetic diversity dynamics in A. c. cerana for further insights. Full article