Flower Pollination Research Articles

Understanding the role of moths (Insecta: Lepidoptera) as pollinators of apple (Malus domestica)

ABSTRACT Given their economic and nutritional value, apples are among the most valuable crops. However, apple production requires cross-pollination. Climate change and various human activities have decreased pollinator diversity in apple orchards, leading many farmers to use honeybee colonies to increase productivity. Studying the pollinators of apple crops is crucial for future pollinator management. While diurnal pollination of apple flowers has been explored, the role of nocturnal pollinators in general and moths in particular has not been thoroughly studied. To assess the role of moths in apple pollination, we studied the effectiveness of moths and other nocturnal flower visitors in depositing pollen grains on the stigma of apple flowers (single-visit pollen deposition) at Shergaon Apple Garden, Arunachal Pradesh (East Himalaya, India). The mean single-visit pollen deposition on apple flowers by nocturnal flower visitors is calculated to be 112.92 ± 10.98. Among all nocturnal flower visitors, moths are the most effective pollen depositors with a mean single-visit pollen deposition of 152 ± 19.9. Thirty moth species are found to carry apple pollen on their proboscides. Of them, three species Nyctemera arctata Walker, 1856 (Erebidae), Pyrrhia umbra (Hufnagel, 1766) (Noctuidae) and Chiasmia azataria (Swinhoe, 1893) (Geometridae) are found to be the highest carriers of apple pollen load. This study indicates that moths are the most significant for nocturnal pollination of apple.

Off-target drift of the herbicide dicamba disrupts plant-pollinator interactions via novel pathways.

While herbicide use in agriculture is expected to have many effects on surrounding weed communities, it is largely unknown how plant exposure to sublethal doses of herbicide may subsequently impact plant-pollinator interactions. We tested the hypothesis that sublethal herbicide exposure indirectly alters plant-pollinator interactions through changes in plant traits, and specifically through alterations in floral display. Using a common garden experiment, we exposed 11 weed species to the herbicide dicamba and examined the potential for changes in pollinator abundance and patterns of pollinator visitation as well as alterations to plant traits. We found variation among plant species in the extent of damage from dicamba drift, and variation in size, flowering time, and flower displays, with some plant species showing negative impacts and others showing little effect. Pollinator frequencies were reduced in dicamba-exposed plots, and pollinator visits were reduced for some weed species yet not for others. Structural equation modeling revealed that the relationship between flower display and pollinator visits was disrupted in the presence of dicamba. Our study provides the most comprehensive picture to date of the impacts of herbicide drift on plant-pollinator interactions, with findings that highlight an underappreciated role of services supplied by weedy communities.

Low Abundance of Regular Pollinators and Indirect Competitive Effects of Dominant Small Bees Negatively Affect Passion Fruit Pollination in Smallholder Croplands.

Land-use changes have led to natural habitat loss and fragmentation, favoring the occurrence of dominant bee species in agroecosystems. This has raised concerns on the dominance effects in pollination-dependent crops like passion fruits (Passiflora edulis Sims) in tropical regions. That is because dominant bee species might overlap their foraging time with regular pollinators, potentially impairing crop yield. Our aim was to understand how dominant small bees affect regular pollinators of passion fruit flowers and its implications on crop production for smallholder farmers. We sampled bees on farms cropping yellow passion fruits in the Cerrado, the Brazilian savanna, and established pollination exclusion experiments to evaluate the interacting effects of dominance and bee community composition on crop yield. We observed a low frequency of regular pollinators, while dominant floral visitors were highly abundant. Dominant pollinators highly overlapped their foraging activity with regular pollinators through time. Contrary to our expectations, the dominance of native and non-native bee species did not directly affect the occurrence of regular pollinators nor crop yield. However, we found evidence that exploitative competition may indirectly affect pollination by regular pollinators. Manual and bee pollination combined increased fruit quality, highlighting the potential benefits of diverse pollinator communities for sustainable crop production. Our findings emphasize the need for strategies that can improve the quality and abundance of resources in agroecosystems for regular native pollinators to optimize pollination in passion fruits on smallholder farms and reduce dominance effects caused by small floral-visiting bees.

Application of Flower Pollination Algorithm and its Comparative Analysis for MPPT of Solar Panels Under Partial Shading Conditions

Application of Flower Pollination Algorithm and its Comparative Analysis for MPPT of Solar Panels Under Partial Shading Conditions

Interval-valued optimal control strategies for vaccine distribution and infection awareness investment in preservation technology

Purpose This study aims to explore the global threat of diseases that affect people, such as diarrheal, Hepatitis B, Rotavirus, Measles diseases, emphasizing the integration of disease vaccines into immunization programs globally as recommended by the World Health Organization, resulting in significant case reductions. Design/methodology/approach Notably, it stresses the necessity of raising awareness about diseases and vaccines through promotional efforts alongside effective inventory management because of vaccine perishability, highlighting preservation techniques and cold storage. Addressing environmental concerns, including carbon emissions from vaccine deterioration, the study proposes green technology investments aligned with Sustainable Development Goals to mitigate these impacts. Additionally, advanced optimization algorithms, including ant colony, modified flower pollination, cuckoo search and particle swarm optimization algorithms, are used to optimize pricing, preservation strategies, green investments and replenishment schedules. The research also uses the concept of interval values to enhance the robustness of the optimization framework. Through numerical experiments, the study demonstrates the effectiveness of this dynamic investment approach, providing empirical validation. Findings Furthermore, sensitivity analysis on critical parameters yields valuable insights for decision-makers, underscoring the importance of dynamically managing vaccine inventory. The study offers practical solutions and managerial insights that can inform policy decisions and strategic planning in disease response efforts. Originality/value This study concludes by emphasizing how creative green technology approaches can help decision-makers manage the social and environmental effects of vaccine inventories in the health care of people.

Correlation of Total Polyphenol and Flavonoid Content With Antioxidant Activity of Caucasian Bee Honey.

Honey is a natural product gathered by honeybees from the pollen and nectar of various plants and flowers. The homeland of the Caucasian honey bee, which draws attention with its honey production and is one of the most productive bee races known in the world, is Northeastern Anatolia in Türkiye. This study aims to determine and correlate the phenolic content and antioxidant activity of 54 honey samples obtained from the most important gene centers of the Caucasian bee in Türkiye. Total polyphenol and flavonoid contents and ferric reducing antioxidant power of aqueous honey extracts were in the range of 9.95-66.34 mg GA/100 g, 0.76-14.24 mg Q/100 g, 63.00-150.00 mg T/100 g, respectively. All analysis methods of Caucasian bee honey samples showed statistically significant differences according to location. The variation in phenolic content and antioxidant activities in Caucasian bee honey is attributed to the region's specific geographical location and vegetation. Furthermore, a robust and significant correlation was identified between the antioxidant activity of the honey samples and the total levels of polyphenols and flavonoids. Thanks to these features, Caucasian bee honey can be an important nutritional and commercial product.

A Novel Hybrid Algorithm Based on Butterfly and Flower Pollination Algorithms for Scheduling Independent Tasks on Cloud Computing

A Novel Hybrid Algorithm Based on Butterfly and Flower Pollination Algorithms for Scheduling Independent Tasks on Cloud Computing

A Biomimetic Feature Extraction Method for Product Packaging Colour Based on Flower Pollination Algorithm

A Biomimetic Feature Extraction Method for Product Packaging Colour Based on Flower Pollination Algorithm

Integrated Multiobjective Energy Management for a Smart Microgrid Incorporating Electric Vehicle Charging Stations and Demand Response Programs Under Uncertainty

This paper presents an innovative 24‐h scenario–based microgrid energy management system (MG‐EMS) designed to achieve cost reduction and emission reduction under conditions of uncertainty. Furthermore, a multiobjective hybrid heuristic algorithm, named hybrid multiobjective particle swarm optimization and lightning search algorithm (hMOPSO‐LSA), is introduced to tackle the MG‐EMS problem. This algorithm combines the LSA and the MOPSO algorithm. The MG under investigation comprises photovoltaic (PV) and wind turbine (WT) units, a combined heat and power (CHP) system, and employs multicarrier energy storage technology, specifically, power‐to‐gas (P2G) technology and an electric vehicle (EV) parking lot (PL). Flexible loads are incorporated into the MG to enhance cost and emission reduction through participation in the demand response program (DRP). The proposed MG‐EMS model utilizes probability density functions (PDFs) for modeling uncertainties and employs the Roulette wheel (RW) method for scenario selection. The simulations, carried out in MATLAB, encompass two different sections. In the first part, the accuracy and efficiency of the proposed algorithm were validated by solving the standard DTLZ benchmark functions and comparing the optimization results with those of several other optimization algorithms. In the second part, energy management in the MG was carried out using the proposed MG‐EMS model, solved by the hMOPSO‐LSA algorithm, both without flexible loads and with their inclusion. To provide a comprehensive evaluation, the problem was solved using the proposed hMOPSO‐LSA algorithm and compared against three benchmark algorithms: multiobjective flower pollination algorithm (MOFPA), MOPSO, and multiobjective dragonfly algorithm (MODA). The optimization results demonstrate that hMOPSO‐LSA achieves higher accuracy compared to other algorithms. Furthermore, the findings indicate that the participation of flexible loads in the DRP results in a 6.43% cost reduction and an 8.21% reduction in emissions. Additionally, P2G technology proves effective in cost and emission reduction, contributing 6.87% of the required gas supply within the MG.

An Enhanced Semisteady‐State Jaya Algorithm With a Control Coefficient and a Self‐Adaptive Multipopulation Strategy

This paper introduces the enhanced semisteady‐state Jaya (ESJaya) algorithm, an improved version of the semisteady‐state Jaya (SJaya) algorithm. The ESJaya algorithm uses a control coefficient to regulate the influence of the best solution, achieving a better balance between exploration and exploitation. It also features a self‐adaptive multipopulation strategy and removes absolute value operators in its update equation for improved performance. Using 20 benchmark functions, the ESJaya algorithm is compared with the traditional Jaya, SJaya, and elitism‐based self‐adaptive multipopulation Jaya (SAMPEJaya) algorithms. The ESJaya algorithm ranks first in obtaining optimum values, mean accuracy, and stability, showing better overall performance than the others. The ESJaya algorithm ranks first with an average overall rank of 1.750, followed by Jaya, SAMPEJaya, and SJaya with average overall ranks of 2.650, 2.700, and 2.900, respectively. The Wilcoxon signed‐rank test confirms the statistical significance of the overall rankings. When compared with the Snow Geese Algorithm (SGA), Big Bang–Big Crunch Algorithm (BBBCA), firefly algorithm (FA), bat algorithm (BA), cuckoo search algorithm (CSA), and flower pollination algorithm (FPA), the ESJaya algorithm gives competitive results in terms of obtaining optimum values, mean accuracy, and stability. Using four real‐world engineering problems, the ESJaya algorithm is compared with the recently proposed piranha predation optimization algorithm (PPOA) and four state‐of‐the‐art algorithms, namely, the COLSHADE algorithm, the sCMAgES algorithm, the SASS algorithm, and the EnMODE algorithm. The ESJaya algorithm finds optimum solutions with good accuracy, consistency, and stability. Overall, it has a slower convergence and longer run time due to its multipopulation strategy and its search for the best solution after each solution update. However, it consistently finds more optimal solutions than other improved algorithms.

Cannabis sativa: Polyploidization - Triploid and Tetraploid Production

Cannabis sativa is known for the source of therapeutic compounds, gaining great importance since restrictions on its growth and use are gradually reduced throughout the world. Unintended cross-pollination of Cannabis sativa crops is one of the most important threats to cannabinoid production. Therefore, avoiding pollination of female flowers during the production of Cannabis sativa for phytocannabinoids is a priority for growers of this crop, as pollination has been shown to reduce final yield of phytocannabinoids. Polyploid plants possess three or more sets of homologous chromosomes and are sterile. Ploidy manipulation has been used in other crops to improve agronomic traits, reduce fertility and produce sterile plants. A promising approach for cannabis breeding involves the process of polyploidization, in which the number of copies of each chromosome is increased. Polyploidization not only causes seedlessness but also brings other physiological and morphological changes to plants, including the plant architecture. Triploid plants are usually both male and female sterile and seeds are not viable or not present. Seedlessness is a highly desirable characteristic for consumers. Polyploid plants often exhibit altered traits, such as larger cell sizes, increased biomass, and changed morphology, which can have significant implications for cannabis industries. Polyploid cannabis plants offer a host of benefits, and they may help growers to overcome the fertilization issue when it comes to cultivating cannabis on a large scale. The strong polyploidy inducing agents in plants are either chemical (Colchicine, Oryzalin, Caffeine, Trifuralin, or phosphoric amides) or gaseous i.e. Nitrous oxide. Polyploid cannabis could become another option for commercial and small-scale growers looking to produce seedless weed. The research lays important groundwork for the development of improved cannabis strains and novel germplasm for breeding efforts.

Microbiome of honey bee corbicular pollen: Factors influencing its structure and potential for studying pathogen transmission.

Honey bees are exposed to a diverse variety of microbes in the environment. Many studies have been carried out on the microbiome of bee gut, beebread, and flower pollen. However, little is known regarding the microbiome of fresh corbicular pollen, which can directly reflect microbes acquired from the environment. Moreover, although evidences have suggested that floral resources in general can affect the bee-acquired microbes, whether specific forage plants affect the composition of these microbes is still unclear. Here, we characterized both the microbiome and plant composition of corbicular pollen in collection seasons over two years from six hives using 16S rRNA gene and ITS2 metabarcoding. The results reveal temporal changes in the microbiome and plant composition in corbicular pollen, which was influenced by environmental factors and the choice of forage plants. We identified several co-occurrences between plant and bacterial genera, indicating specific plant-microbe interactions. Many Spiroplasma species with various insect hosts, including a honey bee pathogen Spiroplasma melliferum, were shown to positively correlate with Rubus, suggesting this plant genus as an important node for microbial transmission. Overall, we demonstrated the potential of corbicular pollen for studying the transmission of microbes, especially pathogens. This framework can be applied in future research to explore the complicated pollinator-microbe-plant network in different ecosystems.

An efficient rumor detection model based on deep learning and flower pollination algorithm

An efficient rumor detection model based on deep learning and flower pollination algorithm

Multi-objective optimization of truss structures using the enhanced Lichtenberg algorithm

Abstract The primary objective of numerous optimization problems is to enhance a single metric whose lowest or highest value accurately reflects the response quality of a system. However, in some instances, relying solely on one metric is not practical, leading to the consideration of multi-objective (MO) optimization problems that aim to improve multiple performance indicators simultaneously. This approach requires the use of a multi-objective optimization method adept at handling the intricacies of scenarios with various indices. Consequently, researchers have not explored multi-objective truss optimization as extensively as single-objective (SO) scenarios. The novel multi-objective Lichtenberg algorithm with two archives (MOLA-2arc) has been developed to address this. The efficacy of MOLA-2arc is evaluated against eight other MO algorithms, including the multi-objective bat algorithm (MOBA), multi-objective crystal structure algorithm (MOCRY), multi-objective cuckoo search (MOCS), multi-objective firefly algorithm (MOFA), multi-objective flower pollination algorithm (MOFPA), multi-objective harmony search (MOHS), multi-objective jellyfish search (MOJS) algorithm, and the original multi-objective Lichtenberg algorithm (MOLA). The challenge is to minimize structural mass and compliance while adhering to stress limitations. The outcomes demonstrate that MOLA-2arc shows notable improvements over its predecessor, MOLA, and surpasses all other competing algorithms in this study.

A modified petal and stamen dimorphism interact to enhance pollen placement by a buzz-pollinated flower.

Floral adaptations supposedly favour pollen grains to cross the numerous barriers faced during their journey to stigmas. Stamen dimorphism and specialized petals, like the cucculus in the Cassieae tribe (Fabaceae), are commonly observed in flowers that offer only pollen as a resource for bee pollinators. Here, we experimentally investigated whether the stamen dimorphism and cucculus enhance pollen placement on the bee's body. We used 3D-printed bee models to apply artificial vibrations on the flowers of Chamaechrista latistipula with their cucculus deflected or maintained in its original position and their anther pores manipulated. After each simulated flower visit, we captured photographs of the artificial bee from four distinct angles. Employing digital imaging techniques, we documented the presence and location of pollen and stigma on the bee's body. Our findings reveal that the cucculus redistributes pollen grains on the bee's body. There is a remarkable increase in pollen density (approximately tenfold) on the lateral side adjacent to the cucculus, precisely where the stigma contacts the bee when the cucculus is unmanipulated. Furthermore, the cucculus also enhances pollen placement on the ventral region of the bee, indicating its additional function. The cucculus also increases the accuracy of pollen grains on the adjacent lateral region of the bee's body, irrespective of the pollen grains released by small or large anthers. Floral specialized traits, such as modified petals and stamen dimorphism, can modify the fate of pollen grains and ultimately contribute to the male reproductive performance in pollen flowers with poricidal anthers. The cucculus exhibits a dual role by promoting pollen placement in optimal regions for pollination and probably supporting pollen grains for bee feeding. These findings provide valuable insights into the adaptive significance of floral traits and their impact on the reproductive success of pollen flowers.

Novel variable charging pricing strategy applied to the multi-objective planning of integrated fast and slow electric vehicle charging stations and distributed energy resources

Electric vehicle charging stations are increasingly being deployed to accommodate the rising demand for electric vehicles. However, this additional load on the distribution system can lead to significant challenges, including increased power losses and undervolage issues. Therefore, this paper proposes the optimized planning of electric vehicle charging stations, along with the integration of distributed energy resources considering the power system operator’s perspective. The approach focuses on minimizing the costs associated with public charging stations and distributed generators, as well as reducing power losses and voltage deviation of the system. Additionally, this study incorporates both fast and slow chargers to address user requirements, while promoting the use of slow charging to help reduce the peak load demand caused by the electric vehicle charging stations. A novel variable pricing strategy for slow charging is also proposed to decrease the operator’s expenses while promoting this mode of charging. The proposed methodology is validated through the integration of a 33-bus distribution test system with a 25-node traffic system. The analysis was conducted using three multi-objective optimization methods: Multi-Objective Cuckoo Search, Multi-Objective Flower Pollination Algorithm, and Non-dominated Sorting Genetic Algorithm II. From the results, it is possible to conclude that the proposed approach provides benefits not only to the system operator but also to EV users, given the reduction in their travel costs. Despite the increased charging demand, the optimization results show a reduction in voltage deviation by 11.57%–16.94% and power losses by 19.71%–24.20% compared to the original system.

Phytochemical S-methyl-L-cysteine sulfoxide from Brassicaceae: a key to health or a poison for bees?

Intensive agricultural practices impact the health and nutrition of pollinators like honey bees (Apis mellifera). Rapeseed (Brassica napus L.) is widely cultivated, providing diverse nutrients and phytochemicals, including S-methyl-L-cysteine sulfoxide (SMCSO). While the nutritional impact of rapeseed on bees is known, SMCSO's effects remain unexplored. We examined SMCSO and its related metabolites-3-methylthiolactic acid sulfoxide and N-acetyl-S-methyl-L-cysteine sulfoxide-analysing their seasonal fluctuations, colony variations and distribution in body parts. Our findings showed that these compounds in bee gut vary among colonies, possibly due to the dietary preferences, and are highly concentrated in bodies during the summer. They are distributed differently within bee bodies, with higher concentrations in the abdomens of foragers compared with nurses. Administration of SMCSO in a laboratory setting showed no immediate toxic effects but significantly boosted bees' antioxidant capacity. Long-term administration decreased bee body weight, particularly in the thorax and head, and altered amino acid metabolism. SMCSO is found in the nectar and pollen of rapeseed flowers and highly accumulates in rapeseed honey compared with other types of honey. This study reveals the dual impact of SMCSO on bee health, providing a basis for further ecological and physiological research to enhance bee health and colony sustainability.

Adjusted linear quadratic regulator-proportional-derivative control of Quanser’s three degrees of freedom helicopter based on flower pollination algorithm under external disturbances

External disturbances, saturation of actuator motors, and limits of certain angular movements are commonly encountered in robotic systems, particularly those involving flight, and they present the most common and influential factors affecting the stability and performance of these systems. In this paper, a hybrid controller for a three-degree-of-freedom (3-DoF) helicopter is designed and applied to this flying robot system, taking into account the previously mentioned constraints. The proposed hybrid controller integrates proportional-derivative (PD) control with an adjusted linear quadratic regulator (ALQR) using the flower pollination algorithm (FPA) optimization method. Simulation results of travel (λ), elevation (ε), and pitch (ρ) responses, as well as experimental results of elevation and travel tracking responses under external disturbances using the bench-top Quanser’s (3-DoF) helicopter, demonstrate the robustness and good performance of the controlled system using the proposed method. The effectiveness of the proposed method is compared to several methods in the literature.

A new hybrid swarm intelligence-based maximum power point tracking technique for solar photovoltaic systems under varying irradiations

Partial shading condition (PSC) adversely affects the maximum power production from the solar array. To overcome this issue, this study has proposed a new hybrid swarm intelligence-based maximum power point tracking (MPPT) algorithm namely marine predator algorithm-particle swarm optimization (MPA-PSO). The novel MPA-PSO is implemented on a recently proposed 4 × 4 permutation combination-based solar topology (PCR). The proposed MPA-PSO improves the efficacy of MPA by updating the velocity in three successive steps; low-velocity phase (v = 0.1), unit velocity phase (v = 1), and high-velocity phase (v ≧10) respectively. Later the global searching and local searching ability is confirmed by PSO. Thus, the novel proposed MPA-PSO improves the optimization efficiency of MPA and updates the position of the PSO algorithm with the MPA algorithm leading to an effective handling of exploration and exploitation phases. The novel MPA-PSO overcomes the challenges of long convergence time by decreasing the swarm size. Further, the challenges like capture of global power in the local peaks and slow change of shading patterns are overcome by the novel hybrid MPA-PSO. The performance of the proposed MPA-PSO is compared with MPA, PSO, and influential flower pollination algorithm (IFPA) under various realistic shading patterns. The specific improvements of the novel MPA-PSO include the reduction in convergence time by 9.08 % to 15.16 % and an increment in average power by 67.29 W against MPA, PSO, and IFPA respectively. Thus, the novel hybrid MPA-PSO discloses greater flexibility and versatility over other considered algorithms in this study.

Spray-drying encapsulation of sunflower pollen peptides using carbohydrate polymers: Physicochemical, antioxidant, structural and morphological analysis

Flower pollen collected by honey bees is a rich source of bioactive peptides (BPs). Different carriers such as maltodextrin (MD), gum Arabic (GA), and β-cyclodextrin (βCD) and their combinations were used for encapsulation of BPs from sunflower pollen protein (SPP). Different physicochemical, antioxidant, functional, structural and morphological properties of the obtained microparticles were evaluated. The amino acid composition of SPP and the derived peptides revealed a high proportion of hydrophobic (∼40%), antioxidant (∼18%), and essential (∼31%) amino acids, indicating their biological potential. Among the microencapsulated samples, the MD-βCD composition exhibited the highest production efficiency (∼65%) and improved physicochemical properties, such as microbial stability, density, flowability, wettability, and solubility, compared to the spray-dried free peptides (free SD-BPs) without a carrier. The encapsulation process helped to maintain the structural integrity of the microparticles and reduced moisture absorption. The SPP-derived peptides encapsulated in MD-βCD also demonstrated potent antioxidant activity, inhibiting ABTS+ (∼75%) and DPPH- (∼69%) free radicals. The results suggest that the BPs obtained from sunflower pollen can be a valuable ingredient for enriching various food products due to their favorable nutritional, functional, and antioxidant properties.