• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1125-1134
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• 2023

Out of the total 17,000 reservoirs in Korea, 13,600 small agricultural reservoirs do not have hydrological measurement facilities, making it difficult to predict water storage volume and appropriate operation. This paper examined univariate and multivariate long short-term memory (LSTM) modeling to predict the storage rate of agricultural reservoirs using remote sensing and artificial intelligence. The univariate LSTM model used only water storage rate as an explanatory variable, and the multivariate LSTM model added n-day accumulative precipitation and date of year (DOY) as explanatory variables. They were trained using eight years data (2013 to 2020) for Idong Reservoir, and the predictions of the daily water storage in 2021 were validated for accuracy assessment. The univariate showed the root-mean square error (RMSE) of 1.04%, 2.52%, and 4.18% for the one, three, and five-day predictions. The multivariate model showed the RMSE 0.98%, 1.95%, and 2.76% for the one, three, and five-day predictions. In addition to the time-series storage rate, DOY and daily and 5-day cumulative precipitation variables were more significant than others for the daily model, which means that the temporal range of the impacts of precipitation on the everyday water storage rate was approximately five days.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.201-214
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• 2023

This study attempted to analyze the impact of individual entrepreneurial capabilities on entrepreneurial intention targeting small and medium-sized business employees, and sought to confirm the mediating effect of entrepreneurial efficacy and entrepreneurial mentoring between entrepreneurial capabilities and entrepreneurial intention. The sub-variables of entrepreneurship competency were analyzed separately into creativity, problem solving, communication, and marketing. 368 questionnaires collected from employees at small and medium-sized manufacturing companies located across the country were used for empirical analysis. A parallel dual mediation model with no causal relationship between parameters was used for empirical analysis using SPSS v26.0 and PROCESS macro v4.2. As a result of the analysis, first, among the start-up competencies, creativity, communication, and marketing were confirmed to have a significant positive (+) effect on start-up efficacy. Second, among the start-up competencies, creativity, communication, and marketing were tested to have a significant positive influence on start-up mentoring. Third, both startup efficacy and startup mentoring were found to have a significant positive influence on startup intention. Fourth, among start-up capabilities, creativity and marketing were confirmed to have a significant positive (+) effect on start-up intention. Fifth, startup efficacy and startup mentoring were found to have a mediating effect on startup intention except for problem solving among startup competencies. As a result, it was confirmed that in order to strengthen the intention to start a business among small and medium-sized business employees, start-up efficacy and start-up mentoring are important factors, and that marketing and creativity have an important influence among individual start-up capabilities, so education and prior preparation for these are necessary. As follow-up research, it will be necessary to apply multivariate models, analyze time series data, research considering external environmental factors, and test the difference between start-up capabilities and performance considering detailed population characteristics.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.193-206
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• 2024

This study aimed to quantify changes in forest cover and carbon storage of Korean Peninsular during the last two decades by integrating field measurement, satellite remote sensing, and modeling approaches. Our analysis based on 30-m Landsat data revealed that the forested area in Korean Peninsular had diminished significantly by 478,334 ha during the period of 2000-2019, with South Korea and North Korea contributing 51.3% (245,725 ha) and 48.6% (232,610 ha) of the total change, respectively. This comparable pattern of forest loss in both South Korea and North Korea was likely due to reduced forest deforestation and degradation in North Korea and active forest management activity in South Korea. Time series of above ground biomass (AGB) in the Korean Peninsula showed that South and North Korean forests increased their total AGB by 146.4Tg C (AGB at 2020=357.9Tg C) and 140.3Tg C (AGB at 2020=417.4Tg C), respectively, during the last two decades. This could be translated into net AGB increases in South and North Korean forests from 34.8 and 29.4 Mg C ha^-1 C to 58.9(+24.1) and 44.2(+14.8) Mg C ha^-1, respectively. It indicates that South Korean forests are more productive during the study period. Thus, they have sequestered more carbon. Our approaches and results can provide useful information for quantifying national scale forest cover and carbon dynamics. Our results can be utilized for supporting forest restoration planning in North Korea

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.315-328
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• 2024

Precipitation is a crucial meteorological variable widely used as essential input data in most hydrological models. However, due to climate change, there is an escalating precipitation variability. Trend analysis plays an important role in planning and operating water resources systems. As recently developed, Innovative Polygon Trend Analysis (IPTA) is useful in identifying and and analyzing the trends of hydrologic variables. In this study, the IPTA was applied to monthly precipitation data obtained from 13 meteorological observatories in Jeolla province, along with synthesized precipitation data according to Shared Socioeconomic Pathways (SSP) scenarios. The trend results were compared those obtained from the Mann-Kendall test and the Sen's slope estimation which are generally used in practice. The results revealed monthly precipitations from February to July and November had increasing trends, and monthly precipitation in October had a decreasing trend. IPTA, Mann-Kendall test, and Sen's slope estimation detected trends in 75.00 %, 5.13 %, and 5.13 % of 156(13 stations × 12 months) time series of monthly precipitation, respectively, which indicates that the IPTA is more sensitive in trend detection compared to the Mann-Kendall test and Sen's slope estimation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Journal of Korean Society of Forest Science
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• v.65 no.1
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• pp.80-91
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• 1984

The purposes of this study are to analyze and forecast the domestic demand for papers by regression models with time-series data (1965-81). For the period of 1965-81, the real GNP of Korea grew at annual average increase rate of 8.8 percent. On the other hand, the domestic demand of papers grew at annual average increase rate of 17.9 percent in this period. Especially, the annual average increase rate for board-papers accounted to 25.8 percent. To analyze domestic demand for papers, GNP, per capita GNP, price findex of papers, production activity index of the major papers consuming industries and price index of substitutive goods were selected as independent variables. The expected values of domestic demand for papers were computed by forecasting equations as follows. T-values are in parentheses. ${\ell}nDDP=2.452+1.986{\ell}nPG-0.844{\ell}nPWI$ $(33.397)^*\;(-6.149)^*\;R^2=0.997$ ${\ell}nDDP=6.468+0.827{\ell}nPDA$ $(17.403)^*\;R^2=0.950$ DDP : Domestic demand for papers PG : Real GNP per capita (1,000 won) PWI : Real price index of papers (1980 = 100) PDAV : Production activity index of the major papers consuming industries The results analyzed and forecasted by these models are summarized as follows: The domestic demand for papers had positive correlation toward per capita GNP and production activity index of the major papers consuming industries. Per capita GNP elasticity of the domestic demand for papers was the most elastic among independent variables. The price elasticity of domestic demand for papers had negative sign and inelastic. These were not only statistically significant but theoretically compatible. The domestic demand for papers was projected to be 3,152-4,470 thousand mit in 1991, representing at annual increase rate of 5.0-12.4 percent during the period of 1982-91. Domestic demand for papers per capita was projected to be 69.1-98.0 kg in 1991.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.