• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Horticultural Science & Technology
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• v.31 no.5
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• pp.531-537
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• 2013

The average annual and winter ambient air temperatures in Korea have risen by 0.7 and $1.4^{\circ}C$, respectively, during the last 30 years. Radish (Raphanus sativus), one of the most important cool season crops, may well be used as a model to study the influence of climatic change on plant growth, because it is more adversely affected by elevated temperatures than warm season crops. This study examined the influence of transplanting time, nitrogen fertilizer level, and climate parameters, including air temperature and growing degree days (GDD), on the performance of a radish cultivar 'Mansahyungtong' to estimate crop growth during the spring growing season. The radish seeds were sown from April 24 to May 22, 2012, at internals of 14 days and cultivated with 3 levels of nitrogen fertilization. The data from plants sown on April 24 and May 8, 2012 were used for the prediction of plant growth as affected by planting date and nitrogen fertilization for spring production. In our study, plant fresh weight was higher when the radish seeds were sown on $24^{th}$ of April than on $8^{th}$ and $22^{nd}$ of May. The growth model was described as a logarithmic function using GDD according to the nitrogen fertilization levels: for 0.5N, root dry matter = 84.66/(1+exp (-(GDD - 790.7)/122.3)) ($r^2$ = 0.92), for 1.0N, root dry matter = 100.6/(1 + exp (-(GDD - 824.8)/112.8)) ($r^2$ = 0.92), and for 2.0N, root dry matter = 117.7/(1+exp (-(GDD - 877.7)/148.5)) ($r^2$ = 0.94). Although the model slightly tended to overestimate the dry mass per plant, the estimated and observed root dry matter and top dry matter data showed a reasonable good fit with 1.12 ($R^2$ = 0.979) and 1.05 ($R^2$ = 0.991), respectively. Results of this study suggest that the GDD values can be used as a good indicator in predicting the root growth of radish.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.358-365
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• 2019

An agrivoltaic systems (AVS) is mixed systems associating photovoltaic panels (PVPs) and crop cultivation at the same time on the given land area. It is receiving attention to improve rural economy. However, it is likely that, the crop yield should be decreased due to the reduced absorption of solar radiation by leaves. Thus, before popularizing the AVS, it is necessary to comprehend the degree of shading by PVPs in AVS. In this study, the change of radiation condition under AVS mimic shading net was investigated. The minimum and maximum of difference of photosynthetically active radiation (PAR) between under and outside shading net were 3.03 mol/㎡/day on a cloudy day and 17.08 mol/㎡/day on a sunny day. This difference decreased when the ratio of diffuse irradiance to global irradiance increased. Such a shading effect resulted in the increase of rice height and decrease of rice tillering.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.749-759
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• 2012

A field experiment was conducted to selection of ground-based remote sensor and reflectance indices to estimate rice production, estimation of suitable season for ground-based remote sensor and N top dressing fertilizer application rate in 2010. Fertilizer application was determined by "Fertilizer management standard for crops" (National Academy of Agricultural Science, 2006). Four levels of N-fertilizer were applied as 0%, 70%, 100% and 130% by base N-fertilizer application and were fertilized as 70% of basal dressing and 30% as top dressing. Rice (Oryza sativa L.) of Chucheong and Joonam (Korean cultivar) were planted on May 22, 2010 in sandy loam soil and harvested on October 6, 2010. Reflectance indices were measured 7 times from July 5 to August 23 by Crop circle-amber and red version and GreenSeeker-green and red version. Remote sensing angle from the sensor head to the canopy of rice was adjusted to $45^{\circ}$, $70^{\circ}$ and $90^{\circ}$ degree because of difference in the density of plant and the sensing angle. The reflectance indices obtained ground-based remote sensor were correlated with the biomass of rice at the early growth stage and at the harvest with $70^{\circ}$ and $90^{\circ}$ degree of sensor angle. The reflectance indices at the 52th Day After Transplanting (DAT) and the 59th DAT, critical season, were positively correlated with dry weight and nitrogen uptake. Specially NDVI at the 59th was significantly correlated with the mentioned parameters. Based on the result of this study, rNDVI by GreenSeeker on $70^{\circ}$ degree of angle at the 59th DAT in Chucheong and rNDVI by Crop Circle on $70^{\circ}$ degree of angle and gNDVI by GreenSeeker on $70^{\circ}$ degree of angle at the 59th DAT in Joonam can be useful for estimation of dry weight and nitrogen uptake. Moreover, sufficiency index estimated by reflectance index at the 59th DAT can be useful for the estimation of N-fertilizer level application and can be used as a model for N-top dressing fertilizer management.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.47-53
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• 2023

With the advent of the 4th Industrial Revolution, diverse and a large amount of data has been accumulated now. The agricultural community has also collected environmental data that affects the growth of crops in smart farms or open fields with sensors. Environmental data has different features depending on where and when they are measured. Studies have been conducted using collected agricultural data to predict growth and yield with statistics and artificial intelligence. The results of these studies vary greatly depending on the data on which they are based. So, studies to enhance data quality have also been continuously conducted for performance improvement. A lot of data is required for high performance, but if there are outlier or missing values in the data, it can greatly affect the results even if the amount is sufficient. So, adjustment of outlier and missing values is essential in the data preprocessing. Therefore, this paper integrates data collected from actual farms and proposes a adjustment system for outlier and missing values based on it.

• Magazine of the Korean Society of Agricultural Engineers
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• v.15 no.2
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• pp.2949-2967
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• 1973

The purpose of this study is to find out and determine the minimum consumptive use of water for Korean cabbage and turmp, so that the minimum water requirement can be secured always for a stable cultivation of these vegetables regardless of weather conditions. The experiment was conducted in two periods; first one from May to July and second one from August to October, each experiment with two varieties of cabbage and two varieties of radish with 2 replicants and 15 treatments. The results found from the above are briefly as follows: 1. Since the mean soil moisture equivalent 64 days after the treatment was 28.5% and the soil moisture content at the time was 2.67% which is far less than that of the wilting point, the crop seemed to be extremely caused by a drought. 2. The rate of 51 days after the seeding, soil moisture content of plot No.2 where irrigation has been continuous was the highest or 21.3%, whereas the plot No.14 without irrigations was 11.2% and the lowest. Therefore, the soil moisture content for the minimum qrowth seemed to be 20%. 3. The consumptive coefficient of Blaney and Criddle on cabbage in two periods were K=1.14 and 0.97 respectively, and on radish in two periods were K=1.06 and 0.86 respectively, thus, cabbage was higher than radish. The consumptive coefficient in the first experiment (May-July) was 0.17 to 0.20 higher than the 2nd experiment(August-October). 4. Nomally, cabbage and radish germinate within one week, however, the germination ot these crops which were treated with a suspended water supply from the beginning took two full weeks. 5. When it elapsed 30 days after seeding, the conditions in plot 1,2 and 3 were fairly good however, the crops in the plops other than these showed a withering and the leaves were withered and changed into high green due to an extrem drought. Though it was about same at the beginning, the drought damage on cabbage was worse than that on radish period, and the reasos for this appears in the latter that the roots are grown too deep. 6. The cabbage showed a high affinity between treated plots and varieties. Consequently, it can be said that cabbage is very suseptive to drought damage, and the yield showed a difference of 35% to 56% depending on the selection oe varieties. 7. The radish also showed a high affinity between the treated plots, however, almost us affinity existed between varieties. Therfore, the yield of radish largely depends on the extent of drought, and the selection of variety does not affect at all. 8. The normal consumptive use on cabbage is $0.62{\ell}/sec$, while that on radish is $0.64{\ell}/sec$, and the minimum optimum water requirement that was obtained in this study is $4,000cc/day/m^3$ or $0.462{\ell}/sec/ha$.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.883-893
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• 2023

While South Korea's dependence on imported grains is very high, droughts impacts from exporting countries have been overlooked. Using the Evaporative Stress Index (ESI), this study globally analyzed frequency, extent, and long-term trends of agricultural droughts and their relation to natural oscillations and global crop prices. Results showed that global-scale correlations were found between ESI and soil moisture anomalies, and they were particularly strong in crop cultivation areas. The high correlations in crop cultivation areas imply a strong land-atmosphere coupling, which can lead to relatively large yield losses with a minor soil moisture deficits. ESI showed a clear decreasing trend in crop cultivation areas from 1991 to 2022, and this trend may continue due to global warming. The sharp increases in the grain prices in 2012 and 2022 were likely related to increased drought areas in major grain-exporting countries, and they seemed to elevate South Korea's producer price index. This study suggests the need for drought risk management for grain-exporting countries to reduce socioeconomic impacts in South Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.127-136
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• 2019

Sweet sorghum (Sorghum bicolor L. Moench), compared to traditional crops, has been evaluated as a useful crop with high adaptability to the environment and various uses, but cultivation has not expanded owing to a lack of related research and information in Korea. This study was conducted to estimate heading date in 'Chorong' sweet sorghum based on climate data of the last 30 years (1989 - 2018) from six regions (Jeonju, Buan, Jeongup, Imsil, Namwon, and Jangsu) in Jellabuk Province. In addition, we compared the growth and quality factors by sowing date (April 10, April 25, May 10, May 25, June 10, June 25, and July 10) in 2018. Days from sowing to heading (DSH) increased to 107, 96, 83, 70, 59, 64, and 65 days in order of the sowing dates, respectively, and the average was 77.7 days. The effective accumulated temperature for heading date was $1,120.3^{\circ}C$. The mean annual temperature was the highest in Jeonju, followed in descending order by Jeongup, Buan, Namwon, Imsil, and Jangsu. The DSH based on effective accumulated temperature gradually decreased in all sowing date treatments in the six regions during the last 30 years. DSH of the six regions showed a negative relationship with mean temperature (sowing date to heading date) and predicted DSH ($R^2=0.9987**$) calculated by mean temperature was explained with a probability of 89% of observed DSH in 2017 and 2018. At harvest, fresh stem weight and soluble solids content were higher in the April and July sowings, but sugar content was higher in the May 10 ($3.4Mg{\cdot}ha^{-1}$) and May 25 ($3.1Mg{\cdot}ha^{-1}$) sowings. Overall, the April and July sowings were of low quality and yield, and there is a risk of frost damage; thus, we found May sowings to be the most effective. Additionally, sowing dates must be considered in terms of proper harvest stage, harvesting target (juice or grain), cultivation altitude, and microclimate.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.386-395
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• 2006

The core questions for determining nitrogen topdress rate (Npi) at panicle initiation stage (PIS) are 'how much nitrogen accumulation during the reproductive stage (PNup) is required for the target rice yield or protein content depending on the growth and nitrogen nutrition status at PIS?' and 'how can we diagnose the growth and nitrogen nutrition status easily at real time basis?'. To address these questions, two years experiments from 2001 to 2002 were done under various rates of basal, tillering, and panicle nitrogen fertilizer by employing a rice cultivar, Hwaseongbyeo. The response of grain yield and milled-rice protein content was quantified in relation to RVIgreen (green ratio vegetation index) and SPAD reading measured around PIS as indirect estimators for growth and nitrogen nutrition status, the regression models were formulated to predict PNup based on the growth and nitrogen nutrition status and Npi at PIS. Grain yield showed quadratic response to PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict grain yield had a high determination coefficient of above 0.95. PNup for the maximum grain yield was estimated to be 9 to 13.5 kgN/10a within the range of RVIgreen around PIS of this experiment. decreasing with increasing RVIgreen and also to be 10 to 11 kgN/10a regardless of SPAD readings around PIS. At these PNup's the protein content of milled rice was estimated to rise above 9% that might degrade eating quality seriously Milled-rice protein content showed curve-linear increase with the increase of PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict protein content had a high determination coefficient of above 0.91. PNup to control the milled-rice protein content below 7% was estimated as 6 to 8 kgN/10a within the range of RVIgreen and SPAD reading of this experiment, showing much lower values than those for the maximum grain yield. The recovery of the Npi applied at PIS ranged from 53 to 83%, increasing with the increased growth amount while decreasing with the increasing Npi. The natural nitrogen supply from PIS to harvest ranged from 2.5 to 4 kg/10a, showing quadratic relationship with the shoot dry weight or shoot nitrogen content at PIS. The regression models to estimate PNup was formulated using Npi and anyone of RVIgreen, shoot dry weight, and shoot nitrogen content at PIS as predictor variables. These models showed good fitness with determination coefficients of 0.86 to 0.95 The prescription method based on the above models predicting grain yield, protein content and PNup and its constraints were discussed.