• Food Science of Animal Resources
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• v.30 no.3
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• pp.487-494
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• 2010

Cooked pork can be easily contaminated with Staphylococcus aureus during carriage and serving after cooking. This study was performed to develop growth prediction models of S. aureus to assure the safety of cooked pork. The Baranyi and Gompertz primary predictive models were compared. These growth models for S. aureus in cooked pork were developed at storage temperatures of 5, 15, and $25^{\circ}C$. The specific growth rate (SGR) and lag time (LT) values were calculated. The Baranyi model, which displayed a $R^2$ of 0.98 and root mean square error (RMSE) of 0.27, was more compatible than the Gompertz model, which displayed 0.84 in both $R^2$ and RMSE. The Baranyi model was used to develop a response surface secondary model to indicate changes of LT and SGR values according to storage temperature. The compatibility of the developed model was confirmed by calculating $R^2$, $B_f$, $A_f$, and RMSE values as statistic parameters. At 5, 15 and $25^{\circ}C$, $R^2$ was 0.88, 0.99 and 0.99; RMSE was 0.11, 0.24 and 0.10; $B_f$ was 1.12, 1.02 and 1.03; and $A_f$ was 1.17, 1.03 and 1.03, respectively. The developed predictive growth model is suitable to predict the growth of S. aureus in cooked pork, and so has potential in the microbial risk assessment as an input value or model.

• Journal of Food Hygiene and Safety
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• v.34 no.4
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• pp.374-379
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• 2019

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures ($10{\sim}25^{\circ}C$) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit ($R^2{\geq}0.92$) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination ($R^2=0.98{\sim}0.99$ for internal validation, 0.97~0.98 for external validation), mean square error (MSE=-0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor ($B_f=0.9918{\sim}1.0066$ for internal validation, 0.9865~1.0205 for external validation), and accuracy factor ($A_f=0.9935{\sim}1.0082$ for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.325-331
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• 2022

One of the most important factors in predicting tomato growth and yield is the leaf area. Estimating leaf area accurately is the beginning of an effective tomato plant growth assessment model. To this end, this study was conducted to identify the most effective model for estimating plant leaf area through the measurement of tomato plant leaves. Leaf area (LA), leaf length (L), leaf width (W), and lamina length (La) were measured for all leaves of 5 plants at two-week intervals. The correlation between LA and tomato-leaf-independent variables showed a strong positive relationship with the formulas La × W, L × W, La + W, and L + W. For LA estimation, a linear model using the formula LA = a + b (La² + W²) gave the most accurate estimation (R² = 0.867, RMSE = 88.76). After examining the positions of upper, middle, and lower leaves from September to December, the coefficient of determination (R²) values for each model were 0.878, 0.726, and 0.794 respectively. The most accurate estimation came from the model that used the upper leaves of the plants. The high accuracy of the upper-leaf-based model is judged by the 50% defoliation performed by farmers after October.

• Smart Media Journal
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• v.11 no.5
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• pp.26-37
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• 2022

In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.275-283
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• 2010

This experiment was conducted to construct process models to estimate grain weight (GW) and grain nitrogen content (GN) in rice. A model was developed to describe the dynamic pattern of GW and GN during grain-filling period considering their relationships with temperature, solar radiation and growth traits such as LAI, shoot dry-weight, shoot nitrogen content, grain number during grain filling. Firstly, maximum grain weight (GWmax) and maximum grain nitrogen content (GNmax) equation was formulated in relation to Accumulated effective temperature (AET) ${\times}$ Accumulated radiation (AR) using boundary line analysis. Secondly, GW and GN equation were created by relating the difference between GW and GWmax and the difference between GN and GNmax, respectively, with growth traits. Considering the statistics such as coefficient of determination and relative root mean square of error and number of predictor variables, appropriate models for GW and GN were selected. Model for GW includes GWmax determined by AET ${\times}$ AR, shoot dry weight and grain number per unit land area as predictor variables while model for GN includes GNmax determined by AET ${\times}$ AR, shoot N content and grain number per unit land area. These models could explain the variations of GW and GN caused not only by variations of temperature and solar radiation but also by variations of growth traits due to different sowing date, nitrogen fertilization amount and row spacing with relatively high accuracy.

• Journal of Food Hygiene and Safety
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• v.22 no.1
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• pp.37-44
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• 2007

The approval of use of certain food-grade phosphates as food additives in a wide variety of meat products greatly stimulated research on the applications of phosphates in foods. Although phosphates have never been classified as antimicrobial agents, a number of investigators have reported that phosphates have antimicrobial activities. Phytic acid is a natural plant inositol hexaphosphate constituting 1-5% of most cereals, nuts, legumes, oil seeds, pollen, and spores. In this study, we investigated antibacterial activities of sodium phytate(SPT), sodium pyrophosphate (SPP), sodium tripolyphosphate (STPP) on Escherichia coli O157:H7 on tryptic soy broth and in beef, pork and chicken. In tryptic soy broth, SPT, SPP and STPP at the concentrations of 0.05, 0.1, and 0.5% effectively inhibited the growth of Escherichia coli O157:H7 in a concentration-dependent manner. The bactericidal activity of SPT was the stronger than that of SPP or STPP at the same concentrations. In addition, the antibacterial effects of SPT, SPP and STPP at the concentrations of 0.05, 0.1, 0.3, and 0.5% on Escherichia coli O157:H7 were also investigated in raw or cooked meats including beef, pork and chicken. SPT, SPP and STPP significantly inhibited the bacterial growth in a dose-dependant manner (p<0.05). The bactericidal effect of SPT was stronger than that of SPP or STPP. The addition of SPT, SPP and STPP in meats increased meat pHs. SPP and STPP also increased the levels of soluble orthophosphate in meats but STP did not. These results indicate that SPT is very effective for inhibition of bacterial growth and that can be used as a muscle food additive for increasing functions of meats.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.3
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• pp.170-178
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• 2016

It is not clear how to apply the climate scenario to assess the impact of climate change in the agricultural sector. Even if you apply the same scenario, the result can vary depending on the temporal-spatial downscaling, the post-treatment to adjust the bias of a model, and the prediction model selection (used for an impact assessment). The end user, who uses the scenario climate data, should select climate factors, a spatial extend, and a temporal range appropriate for the objectives of an analysis. It is important to draw the impact assessment results with minimum uncertainty by evaluating the suitability of the data including the reproducibility of the past climate and calculating the optimum future climate change scenario. This study introduced data processing methods for reducing the uncertainties in the process of applying the future climate change scenario to users in the agricultural sector and tried to provide basic information for appropriately using the scenario data in accordance with the study objectives.

• Asian Journal of Turfgrass Science
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• v.15 no.3
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• pp.127-136
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• 2001

A field trial was initiated to examine the range of inter- and intraspecific variations in morphological and phenological traits with five different accessions of smooth and large crabgrass. In addition, a controlled environment study was conducted to determine the phenotypic plasticity among the accessions of both species in response to 4 daily tempera-ture differentials. In the field experiment, significant inter- and intraspecific variations of smooth and large crabgrass were observed in morphological traits such as leaf length and width. However, most phenological traits were not substantially different between the species and among the accessions of each species. The first seedling emerged at the same time, requiring 9~ 10 days, regardless of the accessions and species. In a controlled environment study, all accessions of each species responded similarly to the 4 temperature differentials in seedling emergence, indicating seedling emergence was not a plastic trait. These results suggest that predicting crabgrass seedling emergence could be independent of geographical regions in the US.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.235-241
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• 2017

This study was carried out to estimate growth characteristics of Kimchi cabbage cultivated in two different growing seasons and three transplanting dates in the greenhouses, and to create a predicting model for the production of Kimchi cabbage based on the growth parameters and climatic elements. Kimchi cabbages were transplanted three times at intervals of two weeks in spring and autumn growing seasons. Sigmoidal models for the estimation of fresh weight (Fw) was designed with days after transplanting, which were Fw=4451.5/[1+exp{-(DAT-34.1)/3.6}]($R^2=0.992$) and Fw=7182.0/[1+exp{-(DAT-53.8)/11.6}] ($R^2=0.979$), respectively. The relationship between fresh weight of Kimchi cabbage and growing degree days (GDD) was highly correlated, and the regression model represented by Fw=4451.5/[1+exp{-(GDD-34.1)/3.6}] ($R^2=0.992$) in spring growing season. The yield of Kimchi cabbage under spring and autumn growing season were estimated 11348.3kg/10a and 15128.2kg/10a, respectively, which were much different than outdoor culture each growing season, while greenhouse cultivation have shown similar results. To estimate the efficacy of prediction yield in Kimchi cabbage, we will need to supplement a predicting model, which was based on the parameters and climatic elements by the field cultivation.