• Journal of Food Hygiene and Safety
• /
• v.36 no.6
• /
• pp.504-509
• /
• 2021

The objective of this study was to develop dynamic model to describe the kinetic behavior of E. coli in sliced smoked duck. E. coli was detected in 2 sliced smoked duck samples (16.7%) at 1.23 log CFU/g. The maximum specific growth rate (𝜇_max) of E. coli ranged from 0.05 to 0.36 log CFU/g/h, and lag phase duration (LPD) ranged from 4.39 to 1.07 h, depending on the storage at 10-30℃, and h₀ value ranged from 0.24 to 0.51. The developed model was validated with observed values obtained at 13℃ and 25℃. The model performance was appropriate with 0.130 of root mean squared error (RMSE), and the dynamic model also described properly kinetic behavior of E. coli in sliced smoked duck samples. These results indicate that E. coli can contaminate sliced smoked ducks and the models developed with the E. coli isolates are useful in describing the kinetic behavior of E. coli in sliced smoked duck.

• Journal of Bio-Environment Control
• /
• v.32 no.4
• /
• pp.434-441
• /
• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Horticultural Science & Technology
• /
• v.32 no.2
• /
• pp.165-170
• /
• 2014

The objective of this study was to make growth and yield models of sowthistle (Ixeris dentata Nakai) by using an expolinear functional equation in a closed-type plant production system. The growth and yield of hydroponically-grown sowthistle were investigated under four different planting distances ($15{\times}10$, $15{\times}15$, $15{\times}20$, and $15{\times}25$ cm). Shoot dry weights per plant was the highest at $15{\times}25$ cm, but was the lowest at $15{\times}10$ cm. Shoot dry weights per area was the highest at $15{\times}15$ cm, but was the lowest at $15{\times}25$ cm. The optimum planting density and planting distance for yield of sowthistle were 44 plants/$m^2$ and $15{\times}15$ cm, respectively. Shoot dry weights per plant and per area were showed as an expolinear type functional equation. A linear relationship between shoot dry and fresh weights was observed to be linear regardless of the planting distance. Crop growth rate, relative growth rate and lost time in an expolinear functional equation showed quadratic function form. Radiation use efficiency of sowthistle was $4.3-6.1g{\cdot}MJ^{-1}$. The measured and estimated shoot dry weights showed a good agreement using days after transplanting as input data. It is concluded that the expolinear growth model can be a useful tool for quantifying the growth and yield of sowthistle in a closed-type plant production system.

• Journal of Bio-Environment Control
• /
• v.26 no.4
• /
• pp.346-352
• /
• 2017

In this study, we analyzed environmental factors including annual fruit growth and meteorological conditions in Suwon area from 2000 to 2014 to develop and verify a fruit width prediction model in 'Fuji' apple. The 15-year average of full bloom data was April 28 and that of fruit development period was 181 days. The fruit growth until 36 days after full bloom followed single sigmoid curve. The environmental factors affecting fruit width were BIO2, precipitation in September, the average of daily maximum and minimum temperature in April, minimum temperature in August, and growing degree days (GDD) in April. Among them, the model was constructed by combining BIO2 and precipitation in September, which are not cross-correlated with each other or, with other factors. And then, the final model was selected as 19.33095 + (5.76242 ${\times}$ BIO2) - (0.01891 ${\times}$ September precipitation) + (2.63046 ${\times}$ minimum temperature in April) which was the most suitable model with AICc of 92.61 and the adjusted $R^2$ value of 0.53. The model was compared with the observed values f rom 2000 to 2014. As a result, the mean difference between the measured and predicted values of 'Fuji' apple fruit width was ${\pm}2.9mm$ and the standard deviation was 3.54.

• Journal of Korean Society of Forest Science
• /
• v.112 no.2
• /
• pp.209-216
• /
• 2023

This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m³ . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1031-1042
• /
• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Food Hygiene and Safety
• /
• v.28 no.3
• /
• pp.217-221
• /
• 2013

This study developed predictive models for the kinetic behavior of Staphylococcus aureus on processed cheeses. Mozzarella slice cheese and cheddar slice cheese were inoculated with 0.1 ml of a S. aureus strain mixture (ATCC13565, ATCC14458, ATCC23235, ATCC27664, and NCCP10826). The inoculated samples were then stored at $4^{\circ}C$ (1440 h), $15^{\circ}C$ (288 h), $25^{\circ}C$ (72 h), and $30^{\circ}C$ (48 h), and the growth of all bacteria and of S. aureus were enumerated on tryptic soy agar and mannitol salt agar, respectively. The Baranyi model was fitted to the growth data of S. aureus to calculate growth rate (${\mu}_{max}$; ${\log}CFU{\cdot}g^{-1}{\cdot}h^{-1}$), lag phase duration (LPD; h), lower asymptote (log CFU/g), and upper asymptote (log CFU/g). The growth parameters were further analyzed using the square root model as a function of temperature. The model performance was validated with observed data, and the root mean square error (RMSE) was calculated. At $4^{\circ}C$, S. aureus cell growth was not observed on either processed cheese, but S. aureus growth on the mozzarella and cheddar cheeses was observed at $15^{\circ}C$, $25^{\circ}C$, and $30^{\circ}C$. The ${\mu}_{max}$ values increased, but LPD values decreased as storage temperature increased. In addition, the developed models showed acceptable performance (RMSE = 0.3500-0.5344). This result indicates that the developed kinetic model should be useful in describing the growth pattern of S. aureus in processed cheeses.

• Journal of Korean Society of Forest Science
• /
• v.106 no.2
• /
• pp.267-273
• /
• 2017

This study was conducted to construct a empirical yield table for Pinus densiflora in real forest. Since existing normal yield tables have been derived by studying and analyzing communities in ideal environment for tree growth, those tables provide more over-estimated values than ones from real forest. Because of this, there are some difficulties to apply the tables to empirical forest except for normal forest. In this study, therefore, we estimated stand growth for real forest on P. densiflora as the representative species of conifers. We used 1,957 sample plot data of P. densiflora in central Korea from National Forest Inventory (NFI) system, and analyzed through estimation, recovery and prediction in order by using Weibull function as a diameter distribution model. Weilbull and Schumacher models were applied for estimating mean DBH and mean basel area and it was found that the site index for P. densiflora in central Korea ranges from 8 to 14 at reference age 30. According to site 12 in the stand yield table, the Mean Annual Increment (MAI) of P. densiflora was $4.42m^3/ha$ at 30 years of age. Compared to existing volume table constructed before, it is showed that MAI of this study were lower. According to the paired t-test that is conducted with the gap of volume values between normal forest and real forest by site index and age, the P-value was less than 0.001 which is recognized to have a statistically significant difference. Based on the results in this study, it is considered to be helpful for practical management and management policy on P. densiflora in central Korea.

• Journal of Climate Change Research
• /
• v.4 no.2
• /
• pp.177-188
• /
• 2013

In this research, forest cover distribution change, forest volume and carbon stock in Yongin-city, Gyeonggi procince were estimated focused on the forest of Yongin-City using forest type map and HyTAG model in relation to climate change. Present forest volume of Yongin-city was estimated using the data from $5^{th}$ Forest Type Map and Korean National Forest Inventory (NFI). And for the future 100 years potential forest distribution by 10-year interval were estimated using HyTAG model. Forest volume was also calculated using algebraic differences form of the growth model. According to the $5^{th}$ Forest Type Map, present needleleaf forest occupied 37.8% and broadleaf forest 62.2% of forest area. And the forest cover distribution after 30 years would be changed to 0.13% of needleleaf forest and 99.97% of broadleaf forest. Finally, 60 years later, whole forest of Yongin-city would be covered by broad-leaf forest. Also the current forest carbon stocks was measured 1,773,862 tC(56.79 tC/ha) and future carbon stocks after 50 years was predicted to 4,432,351 tC(141.90 tC/ha) by HyTAG model. The carbon stocks after 100 years later was 6,884,063 tC (220.40 tC/ha). According to the HyTAG model prediction, Pinus koraiensis, Larix kaempferi, Pinus rigida, and Pinus densiflora are not suitable to the future climate of 10-year, 30-year, 30-year, and 50-year later respectively. All Quercus spp. was predicted to be suitable to the future climate.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.411-411
• /
• 2023

미래 대기 이산화탄소 농도가 증가함에 따라 강수 등 기후의 변화하고, 이는 유출량을 포함한 수문 순환 뿐 아니라 지면 식생 생장에 영향을 줄 것으로 예상된다. 이에 본 연구에서는 미래 CO2 증가에 따른 식생의 변화와 이로 인한 지표 유출량의 변화에 대해 이해하고자 한다. Intergovernmental Panel on Climate Change (IPCC) 6차 평가보고서에서 제시한 표준 온실가스 경로 중 탄소 모듈이 포함된 Coupled Model Intercomparison Project phase 6 biogeochemistry (CMIP6-BGC) 모델과 탄소 모듈이 포함안된 CMIP6 모델 결과를 활용하였다. 공통 사회경제경로 시나리오(Shared Socio-economic Pathway; SSP) 중 고탄소 시나리오인 SSP585에 따른 모델 결과물을 활용하였다. 표면 유출량 자료에 과거 기간 임계수준 방법을 (Threshold Level Method) 적용하여 동아시아 지역 극한 건조 및 습윤 상태의 빈도와 강도를 CMIP6-BGC와 CMIP6에 대해 평가하였다. CMIP6-BGC 경우, 건조 및 습윤 상태의 빈도는 각각 6.17%, 5.03% , CMIP6 경우 각각 9.29%, 6.70% 으로 예측되어, CMIP6-BGC가 CMIP6 보다 극한 상태를 과소평가하는 경향을 보였다. 또한, 잎 면적 지수(Leaf Area Index; LAI), 증산량 등의 변수를 분석하여, 기 도출된 CMIP6-BGC와 CMIP6 간의 극한 건조 및 습윤 상태 예측의 차이가 발생한 메카니즘을 이해하고자 하였다.