• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.107-119
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• 2019

As the development of ICT and integration technology, many changes and innovations in agriculture field are implemented. The agricultural sector has shifted from a traditional industry to a new industrial form called the 6th industry combined with various advanced technologies such as ICT and IT. Various approaches have been attempted to analyze and predict crops based on spatial information. In particular, a variety of research has been carried out recently for crop cultivation and smart farms using drones. The goal of this study was to establish an agricultural drought monitoring system using drones to produce scientific and objective indicators of drought. A soil moisture sensor was installed in the drought area and checked the actual soil moisture. The soil moisture data was used by the reference value to compare and analyze the temperature and NDVI established by drones. The soil temperature by the drone thermal image sensor and the NDVI by the drone hyperspectral was analyzed the correlation between crop condition and soil moisture in study area. To verify this, the actual soil moisture was calculated using the soil moisture measurement sensor installed in the target area and compared with the drone performance. This study using drone drought monitoring system may enhance to promote the crop data and to save time and economy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.156-162
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• 2021

The number of smart farms has increased to save labor in agricultural production as the subsidy become available from central and local governments. The number of illegal greenhouses has also increased, which causes serious issues for the local governments. In the present study, we developed Mask-RCNN model to detect greenhouses based on satellite images. Greenhouses in the satellite images were labeled for training and validation of the model. The Mask-RC NN model had the average precision (AP) of 75.6%. The average precision values for 50% and 75% of overlapping area were 91.1% and 81.8%, respectively. This results indicated that the Mask-RC NN model would be useful to detect the greenhouses recently built without proper permission using a periodical screening procedure based on satellite images. Furthermore, the model can be connected with GIS to establish unified management system for greenhouses. It can also be applied to the statistical analysis of the number and total area of greenhouses.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.184-192
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• 2021

Domestic agriculture is facing real problems, such as a decrease in the population in rural areas, a shortage of labor due to an aging population, and increased risks due to the deepening of climate change. Smart farming technology is being developed to solve these problems. In the development of smart agricultural technology, irrigation control plays an important role in creating an optimal growth environment and is an important issue in terms of environmental protection. This paper is about the study of collecting and analyzing the rhizosphere environmental data of domestic paprika farms for the purpose of improving the quality of crops, reducing production costs, and increasing production. Irrigation control modeling presented in this paper Control modeling is to graphically present changes in a medium weight, feed, and drainage due to regional climatic features. To derive the graph, the parameters were determined through data collection and analysis, and the suggested irrigation control modeling method was applied to the collected rhizosphere environmental data to control irrigation in 6 regions (Gangwon-do, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, and Gyeongnam). The parameters were obtained and graphs were derived from them. After that, a study was conducted to analyze the derived parameters to verify the validity of the irrigation control modeling method and to correlate them with climatic features (average temperature and precipitation).

• Journal of agriculture & life science
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• v.51 no.4
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• pp.139-147
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• 2017

This study was carried out in order to reduce the amount of sawdust for recycling the generated manure from livestock farms, and to investigate the effects on the reducing usage of sawdust and quality of produced compost. To do this, a cylindrical horizontal composting device were used in the experiments and compost was analyzed for judging produce compost quality. The experiment was carried out separately under different cases of operational control conditions. The first case was produced by using sawdust and pig manure mixture(Test-1); the second case was produced by using sawdust, pig manure and the previously produced compost(Test-2). In the second case, Except for some heavy metal content, The water content and C/N ratio were found to be suitable for fertilizer process specification of the RDA(Rural Development Administration) and it was found to reduce the sawdust 1.25tons usage.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.35-43
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• 2021

Citrus quality is generally determined by its sugar content and acidity. In particular, sugar content is a very important factor because it determines the taste of citrus. Currently, the most commonly used method of measuring citrus sugar content in farms is a portable juiced sugar meter and a non-destructive sugar meter. This method can be easily measured by individuals, but the accuracy of the sugar content is inferior to that of the citrus NongHyup official machine. In particular, there is an error difference of 0.5 Brix or more, which is still insufficient for use in the field. Therefore, in this paper, we propose an AI model that predicts the citrus sugar content of unmeasured days within the error range of 0.5 Brix or less based on the previously collected citrus sugar content and meteorological data (average temperature, humidity, rainfall, solar radiation, and average wind speed). In addition, it was confirmed that the prediction model proposed through performance evaluation had an mean absolute error of 0.1154 for Seongsan area and 0.1983 for the Hawon area in Jeju Island. Lastly, the proposed model supports an error difference of less than 0.5 Brix and is a technology that supports predictive measurement, so it is expected that its usability will be highly progressive.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.378-385
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• 2021

The production of plum jangachi requires the cleaning of plums, immersion in saltwater, separation of seed and pulp, removal of impurities, and addition of sugar. In most farms, the separation of plum seed and pulp is carried out manually, requiring considerable labor, which is why plum jangachi is expensive. To solve this problem, this study designed and manufactured automatic, semi-automatic plum seed and pulp separators. During the design process, the characteristics were compared, and the machine power was determined through on-site test after manufacture. As a result, automatic machines used plums 180° arrayed and six reverse-edged blades, semi-automated plums 180° arrayed, and six blades, each with a 68% and 57% pulp recovery rate and a machine power of 80 kg/h and 62 kg/h respectively. Overall, the mechanization of plum processed food will reduce labor and increase the market value of plums compared to the previous method.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.87-97
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• 2021

To investigate the current pest management status in Korea, a survey was conducted from 151 students and graduates in the Korea National College of Agriculture and Fisheries (KNCAF) by on-line. The questionnaire consists of two divisions, basic questions and pest control questions. The basic questions were including the respondent's age, academic status, cultivating crops and cultivating area. The pest control questions were including pest control methods, pesticide selection rationale, and pest forecasting methods. As a summary of basic questions, the respondents in their 20s accounted for 91.2%. Moreover, 34.5% of the respondents had over 3 hectares of cultivating area. The cultivating methods were differed by cultivating crops. As a summary of pest control questions, major control methods were using the conventional chemicals (>66%). To understand the pesticide selection rationale, farmers/respondents made their own decisions based on existing control techniques (30%) or depended on the decisions of pesticide vendors (29%). As for the pest forecasting method, it was mainly conducted by the Rural Development Administration affiliated organization (29%) and the National Crop Pest Management System (27%). Regarding the reliability of the pest diagnosis and pesticide prescription of pesticide vendors, 97% of the respondents marked above average. However, there was no choice on strong reliability. Interestingly, 79% of the respondents agreed to train experts for pest diagnosis and pesticide prescription with high necessity and, in particular, 47% of respondents were very strongly supported. These results suggest that the farmers might be need more qualified experts in pest diagnosis and pesticide prescriptions. Taken together, these survey results would provide important information to understand the current status of pest management by farmers' point of view and useful to set the direction of pest control.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.332-342
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• 2022

Kale (Brassica oleracea var. acephala) is one of the most frequently consumed leafy vegetables globally, as it contains numerous nutrients; essential amino acids, phenolics, vitamins, and minerals, and is particularly rich in glucosinolates. However, the differences in the biosynthesis of glucosinolates and related gene expression among kale cultivars has been poorly reported. In this study, we investigated glucosinolates profile and content in three different kale cultivars, including green ('Man-Choo' and 'Mat-Jjang') and red kale ('Red-Curled') cultivars grown in a vertical farm, using transcriptomic and metabolomic analyses. The growth and development of the green kale cultivars were higher than those of the red kale cultivar at 6 weeks after cultivation. High-performance liquid chromatography (HPLC) analysis revealed five glucosinolates in the 'Man-Choo' cultivar, and four glucosinolates in the 'Mat-Jjang' and 'Red-Curled' cultivars. Glucobrassicin was the most predominant glucosinolate followed by gluconastrutiin in all the cultivars. In contrast, other glucosinolates were highly dependent to the genotypes. The highest total glucosinolates was found in the 'Red-Curled' cultivar, which followed by 'Man-Choo' and 'Mat-Jjang'. Based on transcriptome analysis, eight genes were involved in glucosinolate biosynthesis. The overall results suggest that the glucosinolate content and accumulation patterns differ according to the kale cultivar and differential expression of glucosinolate biosynthetic genes.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.857-862
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• 2021

Recently, rapid climate change has had a significant impact on the bee ecosystem. The decrease in the number of bees and the change in the flowering period have a huge impact on the harvesting of beekeepers. Accordingly, attention is focused on smart beekeeping, which introduces IoT technology to beekeeping. According to the characteristics of beekeeping, it is impossible to continuously observe the beehive in the hive with the naked eye, and the condition of the hive is mostly dependent on knowledge from experience. Although a system that can measure partly through sensors such as temperature/humidity change inside the hive and measurement of the amount of CO2 is applied, there is no research on measuring the movement path and amount of movement of bees inside the beehive. Part of the migration of honeybees inside the hive can provide basic information to predict the most important cleavage time in beekeeping. In this study, we propose a device that detects the movement path of bees and measures and records data entering and exiting the hive in real time. The device proposed in this study was developed according to the honeycomb standard of the existing beehive so that beekeeping farms could use it. The development method used a photodetector that can detect the movement of bees to configure 16 movement paths and to detect the movement of bees in real time. If the measured honeybee movement status is utilized, the problem of directly observing the colony with the naked eye in order not to miss the swarming time can be solved.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.779-783
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• 2021

The amount of sunlight (irradiation) acts as a very important factor for paprika (Capsicum annuum L.) productivity, but there are difficulties in developing a standard model for estimating paprika productivity using irradiation factors. This study was conducted to investigate how to increase the suitability of using irradiation as an independent variable when developing a standard model. In the linear regression analysis using the independent variable (cumulative irradiation) and the dependent variable (cumulative productivity) were classified as the average value of the total farm productivity (MTFP), and above and below (MHFP, MLFP) based on the average value, respectively. The RMSE value of the estimated linear regression model was 0.9418 kg·m^-2 in the MHFP, which was significantly lower than 1.5468 kg·m^-2 in the MTFP and 1.3812 kg·m^-2 in the MLFP. And in due course of time (month), RMSE value was also the lowest in MHFP, below 1.0 kg·m^-2 in all months. Therefore, when developing a regression model for estimating paprika productivity using irradiation, it is judged that it will improve the suitability of the estimation model by classifying and analyzing the difference in productivity of farms with an appropriate method.