• Journal of Apiculture
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• v.33 no.4
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• pp.303-306
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• 2018

Drones of honeybee (Apis mellifera) have been regarded as a useful value only when mating with queen bee. However, the drone pupae have been reported to be nutritionally valuable, and a potential beekeeping product. In this study, drone pupae extracted with 5% acetic acid were used to measure anti-thrombosis related fibrinolytic activity using Strup and Mullertz fibrin plate method. As a result, the drone pupae extract showed higher effect of fibrinolytic activity(clear zone diameter 20.83mm) compared to the human plasmin (clear zone diameter 12.93mm) used as a positive control. It was suggested that the extract of drone pupae can be developed as a functional material helping prevention or treatment of various vascular diseases.

• Journal of Apiculture
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• v.34 no.2
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• pp.161-167
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• 2019

There is an urgent need for novel protein sources as an alternative to meat production. Insects, such as honeybees, hold potential as a safe, nutritious and reliable protein source for the future. In the present study, we established optimal powder preprocessing conditions of drone pupae (Apis mellifera L.) for use as a novel food. The content of moisture, crude protein, crude fat, crude ash, carbohydrate and crude fiber in drone pupae(Apis mellifera L.) were analyzed. The crude protein content ranged from 48.5 to 51.8% was found in both freeze-dried and hot-air powdered drone pupae. However, the protein content in the freeze-dried powder was higher than that in the hot-air powder by 3.3%. According to the Korean Food Standard Codex test method, coliforms, Salmonella spp. Staphylcoccus aureus, and Enterohamorrhagice Escherichia coli were not detected in both freeze-dried and hot-air powder. Therefore, we suggest that the high protein content of the powdered drone pupae prepared in this study can serve as a novel food.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.151-167
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• 2020

This study examined the factors affecting the behavioral intention for agricultural drone service. The survey results of 324 agricultural-related workers were analyzed using SPSS v22.0 and PROCESS macro v3.4. The effects of technology acceptance factors by UTAUT on the behavioral intention for agricultural drone service and the mediating effects of perceived benefits were analyzed. The results are as follows: First, the technology acceptance factors had positive (+) effects on perceived benefits and behavioral intention for agricultural drone service. Second, economics mediated between factors excluding performance expectancy and intention, convenience also mediated between factors excluding social influence and intention, and there was no significant mediating effect of practicality benefits. In the future, a further research is required for people trained in agriculture or drone or had a drone license.

• Journal of Food Hygiene and Safety
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• v.33 no.5
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• pp.399-403
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• 2018

In this study, safety investigations on harmful microorganisms and mycotoxins were conducted on honeybee drone pupae as a new food material, which is rich in nutrients and capable of being mass produced in apiaries. The honeybee drone pupae produced in apiaries were collected from three different regions in Korea and frozen immediately. Subsequently, the samples were subjected to freeze-drying. According to the Korean Food Code test method, coliforms, Salmonella species, Staphylococcus aureus, and enterohemorrhagic Escherichia coli were not detected in 280 honeybee drone pupas. In addition, mycotoxins, aflatoxin $B_1$, ochratoxin A, deoxynivalenol, and zearalenone were not detected. Therefore, it is proposed that the honeybee drone pupae collected from the beehives and immediately frozen as safe from harmful microorganisms and mycotoxins and can be used as a food material.

• International Journal of Industrial Entomology and Biomaterials
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• v.13 no.2
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• pp.57-61
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• 2006

In this study, morphology of perithecia, asci, ascospores, etc. of C. sphecocephala were examined for its telemorphic characteristics. Its colony grew up to 32 mm in diameter on potato dextrose agar (PDA) for 30 days under the condition of $24{\pm}1^{\circ}C$. PDBLA and PDBAA media were selected as optimal media for C. sphecocephala, on which the growth was 1.5 times as fast as on PDA medium. Moreover, PDBLA medium induced successfully the synnemata of anamorphic state. C. sphecocephala was able to be proliferated in vitro on both larva and adult of honeybee drone as its substrate. After inoculated onto the drone larva, it produced mycelium at $24{\pm}1^{\circ}C$, with the maximum yield up to $67{\pm}3mg$ on the $50^{th}$ day.

• Journal of Drive and Control
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• v.20 no.3
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• pp.25-34
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• 2023

In this study, we conducted a research on optimizing the spraying performance of agricultural drones and predicted the spraying performance in various flight conditions using the multi-layer perceptron (MLP). Data was collected using a test device for pesticide spraying performance according to the water sensitive paper (WSP) evaluation. MLP training involved supervised learning to achieve a coefficient of variation (CV), which indicates the degree of uniform spraying. The performance evaluation was conducted using R-squared (R²), the test samples showed an R² of 0.80. The results of this study showed that drone spraying performance can be predicted under various flight environments. In addition, the correlation analysis between flight conditions and predicted spraying performance will be useful for further research on optimizing the spraying performance of agricultural drones.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.5
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• pp.51-65
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• 2024

With the advancement of Unmanned Aerial Vehicles (UAV) technology, aerial spraying has been rapidly increasing in the agricultural field. Drones offer many advantages compared to traditional applicators, but they pose challenges such as spray drift risk and spray uniformity. To address these issues, it is essential to understand the characteristics of complex airflow generated by drones and its consequences for the spray performance. This study aims to identify the air velocity distribution of drone downwash and the resulting spray deposition distribution on the ground, ultimately proposing optimized spraying widths and criteria. Experiments were conducted using two agricultural drones with different propeller arrangements under various flight and measurement conditions. The results showed that during hovering, the downward airflow affected the area within a distance of the radius of the blade (R) from the center of the drone. When the drone was flying, the downward airflow was effective up to a distance of 2R. Droplet deposition was concentrated at the center of the drone during hovering. However, during flying, the droplet deposition was more evenly distributed up to the distance of R. The drone downwash and droplet deposition were significantly different during flying compared to the hovering state. At an effective spray width of 3R, the coefficient of variation (CV) was generally less than 16%, indicating a significant improvement in spray uniformity. These findings help optimize effective spraying techniques in drone-based applications.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.21-31
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• 2018

The result of examination of diagnostic method, which is composed of a combination of a thermal camera and a drone that visually shows the temperature of the object by detecting the infrared rays, for detecting the leakage of earth dam was driven in this research. The drone infrared thermography method was suggested to precise safety diagnosis through direct comparing the two method results of electrical resistivity survey and thermal image survey. The important advantage of the thermal leakage detection method was the simplicity of the application, the quickness of the results, and the effectiveness of the work in combination with the existing diagnosis method.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.