Search Results (303)

Apiculture, or beekeeping, refers to the cultivation and management of honey bees for honey and byproducts, including the extraction, bottling, and sale of hive products such as honey, propolis, royal jelly, bee venom, bee pollen, bee bread and other fermented bee products [...] 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

In the current study, the impact of different acaricide treatments against Varroa (such as amitraz strips, oxalic and formic acid strips impregnated with glycerin, or the sublimation or instillation of oxalic acid) on glycerol residue levels of propolis was investigated. Propolis samples were collected from five beehives located in Chalkidiki (northern Greece), where all mentioned treatments were used and chemically analyzed and compared to control ones. Propolis samples were collected on days 7, 21, and 65 after the application of Varroa treatments, extracted with ethanol/water (70:30), silylated, and then analyzed using GC-MS, showing the predominance of diterpenes. The sublimation of oxalic acid and the amitraz treatment yielded a low glycerol residue (5.12% and 5.09% from 9.98% and 9.19%, respectively) in propolis specimens, while glycerin-impregnated oxalic acid strips led to elevated glycerol percentages (24.30% from 20.51%), unlike the reduced glycerol residues for all other treatments (instillation: 12.60% from 14.48% and glycerin-impregnated formic acid strips: 8.91% from 9.25%) and controls (3.27% from 6.30%). Furthermore, Principal Component Analysis (PCA) and the corresponding biplot illustrated how the sample composition varied across treatments and sampling days, highlighting the chemical constituent categories that most strongly contributed to these distinctions. These findings suggest that the use of glycerol-impregnated strips should be avoided in future beekeeping treatments against varroosis, as they could have a negative impact on the quality of propolis either for nutritional or medicinal applications. Full article

Honey production has been an integral part of the UAE’s heritage. Vachellia tortilis and Ziziphus spina-christi pollen and nectar are essential components of high-quality UAE honey. These plants are integral to Emirati culture, showcasing a legacy of ecological balance and medicinal uses. In addition to their cultural significance, V. tortilis and Z. spina-christi offer substantial pharmacological and ecological value. This review explores the role of V. tortilis and Z. spina-christi in producing honey rich in bioactive compounds with antimicrobial, antioxidant, and anti-inflammatory properties, highlighting their therapeutic potential in addressing infectious and chronic diseases. Furthermore, the diversity of phytochemicals in the honey from these plants supports their use in pharmaceutical advancements, including cancer and antibacterial treatments. Their apicultural importance is also emphasized, particularly in supporting sustainable honey production systems adapted to arid environments. The eco-friendly production of silver nanoparticles from Z. spina-christi demonstrates their versatility for health and agriculture. By exploring views on honey authenticity, advanced extraction methods, and the medicinal benefits of honeybee products, this study promotes these species’ conservation and sustainable use. The study emphasizes the contributions of V. tortilis and Z. spina-christi to ecological stability, public health, and economic growth. It presents a compelling case for leveraging their potential to advance sustainable apiculture and ecosystem management in arid regions. Full article

With the vigorous development of sericulture, the traditional labor-intensive small-scale silkworm rearing has been progressively transformed into a large-scale mechanized system. Consequently, silkworm factories can produce cocoons continuously throughout the year. However, this intensive production pattern generates a substantial amount of silkworm excrement. If not managed properly or disposed indiscriminately, silkworm excrement can lead to severe environmental pollution. In recent years, increasing attention has been paid to the comprehensive utilization of this bioresource. Numerous studies have explored its potential in a wide range of applications. This review systematically summarizes current research on silkworm excrement utilization, particularly focusing on its fundamental characteristics, key technologies, and application areas. Future efforts should aim to promote efficient resource recycling and support the development of sericulture. Full article

Varroa destructor represents a major threat to honeybee colonies worldwide, prompting the search for alternative organic acaricides. This study evaluated the biological activity of essential oils extracted from three Patagonian wild plants—Adesmia boronioides, Dysphania multifida, and Senecio filaginoides—on both V. destructor and Apis mellifera. Chemical analysis revealed that A. boronioides oil was dominated by esquelenone (34.49%), D. multifida by ascaridole (34.87%), and S. filaginoides by α-pinene (40.87%). All essential oils exhibited acaricidal activity, with D. multifida showing the lowest LC₅₀ against V. destructor (1.1 µL/mL at 24 h). Toxicity assays on adult bees indicated that A. boronioides and D. multifida significantly reduced bee survival, whereas S. filaginoides did not appear to cause significant mortality (LC₅₀ = 139.5 µL/mL). Repellency tests for A. boronioides and D. multifida showed significant mite repellence. Larval assays revealed a high survival rate under S. filaginoides treatment (survival rate > 80.24%), in contrast to the reduced viability observed with the other oils. The high selectivity index of S. filaginoides underscores its potential as a selective and safe botanical acaricide. Moreover, its LC₅₀ decreased over time, suggesting a residual acaricidal effect. These findings support S. filaginoides as a promising candidate for sustainable V. destructor control. Full article

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

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

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

In the context of a rapidly expanding global population, mulberry leaf protein emerges as an emerging source of plant protein, with most applications currently at Technology Readiness Level (TRL), presenting substantial potential for application in functional foods and nutraceuticals. This paper analyzes three key advantages of mulberry leaf protein. Firstly, the abundant and inexpensive production of mulberry leaves establishes a solid foundation for large-scale protein extraction. Secondly, advancements in the preparation processes and production technology for mulberry leaf protein have further enhanced its viability. Thirdly, mulberry leaf protein boasts excellent nutritional value and outstanding functional properties, along with multiple biological activities, including antioxidant effects, aging delay, and blood-pressure-lowering activity. These superior qualities considerably broaden its range of applications. Furthermore, this paper evaluates existing research (before 30 June 2025) while exploring prospective avenues for future investigation. The findings of this review are important for enhancing the understanding of the potential applications of mulberry leaf protein in food science and nutrition. The aim is to provide new ideas for the efficient utilization of mulberry leaf protein resources and the establishment of a global food security system. Full article

Royal jelly (RJ), a secretion from nurse bees, is a key factor in honeybee caste differentiation and a high-value product in apitherapy. Despite its economic and biological importance, factors affecting its yield and composition remain insufficient. This study investigated the impact of grafted larval age and sex and the collection day of RJ on its yield and physicochemical characteristics. Three independent experiments were conducted using strong Apis mellifera L. colonies. Larvae of different ages (first, second, and third) were grafted, and RJ was harvested 24, 48, and 72 h post grafting. Additionally, worker and drone larvae were used to assess the effect of larval sex. RJ was analyzed for moisture, protein, sugar, and 10-hydroxy-2-decenoic acid (10-had) content. Results showed that RJ yield significantly increased with collection day, with the third day being optimal. Protein content declined over time, while moisture content rose, although sugar levels and 10-HDA remained stable. Second-day larvae yielded the highest RJ volume without affecting composition. Larval sex did not significantly influence either RJ yield or composition. The results of this study may provide valuable insights into the quality determinants of royal jelly, enabling beekeepers to optimize production for both enhanced royal jelly yield and the rearing of higher-quality queen bees. Full article

Due to the potential role of Auchenorrhyncha in the transmission of the bacterium Xylella fastidiosa in a wide variety of cultivations, during recent years in Europe, many studies have focused on species composition, abundance and seasonal appearance of Auchenorrhyncha. However, females and nymphs are difficult to identify, as species-level identification relies primarily on male genitalia morphology. Sampling was conducted over four years in olive fields in Lesvos Island, in the Northeast Aegean, Greece, using sweep nets and Malaise traps. Both adults and nymphs were collected, with males identified to species level, while females and nymphs were separated on different morphotypes. Representatives from each morphotype and identified adults were sequenced using the mitochondrial cytochrome oxidase subunit I (COI) gene. Using a classical morphological approach, 58 species were identified to species level, and using DNA barcoding, nymph morphotypes and females were successfully identified within the families Cicadellidae, Aphrophoridae, Delphacidae and Issidae. A phylogenetic tree was generated, clustering nymphs together with the corresponding adults. Our results demonstrate the utility of combining morphological and molecular methods for accurate species identification and highlight the importance of enriching online databases with additional species records. Full article

To solve the technical bottleneck caused by the absence of a feasible method for removing the spermatheca in social insects, we developed a microsurgical technique specifically designed for bumblebee females. In this study, the invention of this technique is based on the anatomical characteristics of the sting chamber of bumblebees and uses a bespoke scalpel to precisely remove the spermatheca, which is small in size and deeply embedded within the body. During the removal operation, a small wound was observed and a small amount of hemolymph flowed out. The wound healed very quickly and the survival rate of treated individuals was high. The results showed that there was no significant impact on the critical life activities of queens and workers, including longevity, mating behavior, oviposition capacity, and overwintering survival rate after the spermatheca was removed using this technique. These findings further confirm the feasibility and applicability of the technique and provide strong technical support for exploring the evolutionary dynamics and potential function of the spermatheca in social insects. 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