• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.27-42
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• 2017

The global rise in atmospheric $CO_2$ concentration and its associated climate change have significant effects on agricultural productivity and hydrological cycle. For food security and agricultural water resources planning, it is critical to investigate the impact of climate change on changes in agricultural productivity and water consumption. APEX-Paddy model, which is the modified version of APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystem, was used to evaluate rice productivity and evapotranspiration based on climate change scenario. Two study areas (Gimjae, Icheon) were selected and the input dataset was obtained from the literature. RCP (Representitive Concentration Pathways) based climate change scenarios were provided by KMA (Korean Meteorological Administration). Rice yield data from 1997 to 2015 were used to validate APEX-Paddy model. The effects of climate change were evaluated at a 30-year interval, such as the 1990s (historical, 1976~2005), the 2025s (2011~2040), the 2055s (2041~2070), and the 2085s (2071~2100). Climate change scenarios showed that the overall evapotranspiration in the 2085s reduced from 10.5 % to 16.3 %. The evaporations were reduced from 15.6 % to 21.7 % due to shortend growth period, the transpirations were reduced from 0.0% to 24.2 % due to increased $CO_2$ concentration and shortend growth period. In case of rice yield, in the 2085s were reduced from 6.0% to 25.0 % compared with the ones in the 1990s. The findings of this study would play a significant role as the basics for evaluating the vulnerability of paddy rice productivity and water management plan against climate change.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.259-274
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• 2017

To get some informations for sustainable paddy use, the productivities of soils with two years of cropping systems were estimated through pot experiment using two pretreated groups of not autoclaved 'natural'- and 'autoclaved'-soils without any fertilization. And then the relationship between the productivities, called yield potentials, and the characteristics of soils as affected by cropping systems, such as rice-rice (R-R), ricebarley-rice-barley (R-B-R-B), rice-barley-rice-wheat (R-B-R-W), soybean-barley-soybean-barley (S-B-S-B), of which barley and wheat were composted at a level of $10MT\;ha^{-1}$, and S-B-S-B without compost, was analyzed. These treatments were established in mid-mountainous loam paddy, which contained exchangeable Ca of $11.8cmol_c\;kg^{-1}$, located at the altitude of 285 m above sea level in Sangju of Korea. Crops for the estimation of soil productivity were rice cv. 'Seolemi' and soybean cv. 'Chamol'. As a result, under the natural soils condition, rice grain and straw were highly produced in composted S-B-S-B soils (p < 0.05) and lowly in R-R soils (p < 0.05). While soybean grain and stem were higher in R-R soils (p < 0.05) than other soils which not significantly different each other. In case of autoclaved soils, the yield potentials of rice and soybean were high together in either composted R-B-R-B/W or S-B-S-B soils compared to R-R and uncomposted S-B-S-B soils (p < 0.05). In especial, these yield potentials under the natural soils condition were commonly influenced by soil porosity showing negative correlation for rice (p < 0.01); positive for soybean (p < 0.05). And the porosity possibly reversed even the symbiotic contribution of indigenous Bradyrhizobium japonicum for soybean. Under autoclaved soils condition the potentials of rice and soybean showed negative correlations with soil C:N ratio (p < 0.05) similarly to the case of rice in the natural soils.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.75-86
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• 2020

The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAV multi-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effects according to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. The correlation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As a result of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, it is considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimate the damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations in early diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf area ratio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this study are expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.

• Korean Journal of Soil Science and Fertilizer
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• v.9 no.3
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• pp.133-147
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• 1976

1. The purpose of this study is to provide a series of statistical "bench marks" from which one can begin to think systematically about the required development of the Korean food and fertilizer needs over the next quarter-century. 2. The Korean population has been estimated by the characteristics of the population and its social and political situations today. Because fertility and mortality rates are relatively stable and are under control of politics concerned, the estimation rates were established with 1.6% over 1975-1980, 1.3% over 1981-1990, and 1.0% over 1991-2000. 3. Annual per capita absorption of milled rice has fluctuated rather closely around 140kg, since 1968, with no evidence of declining trend. Per capita absorption of barley and wheat around 120 kg, and legumes around 10.6kg, However because the case of wheat and corn productions are rather difficult the self-sufficiency in the future, the rice is considered to be accelerate its yield growth surplus the level of self-sufficiency to export. 4. The fertilizer demand in each element has been calculated by mechanical multiplication of "the recommend index of fetilizer application" to yield a unit production over the need of national food supply by crop year. 5. As a results refer to Table (8), the estimated quantities of total fertilizer demand to meet the national food supply of the years of 1974, 1980, 1985, 1.990, 1995, and 2000 are reached around 871500, 1138150, 1375480, 1515030, 1652090 and 1799850 metric tons in each year.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.2
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• pp.139-145
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• 2015

Rice ratooning is the production of a second rice crop from the stubble left behind after the main-crop harvest. The main advantage of rice ratooning is that in areas where rice is the main crop, double crop of rice can be grown for additional returns. Eight cultivars of rice were tested for estimation their ratooning ability. The main crop was harvested at mass maturity, after which the tillers were mowed to stubbles of about 10 cm tall. And then left without any further input, until the ratooned plant were ready for harvest. Highly significant variations were detected in the ratoon performance among cultivars, with ratoon ability ranging from 0% ('Unkwang', 'Jopeyong', 'Odae', 'Nokyang') to 33% ('Jinbuol') in their grain yield. The maximum grain yield from ratoon rice was 202 and 203 kg/10a for 'Jinbuol' and 'Joun' followed by 'Junamjoseng' 174kg/10a. Protein and amylose contents of ratoon rice were more increased than those of main rice. The platability value of cooked rice of ratoon was lower than that of main crop. Germination rate of the previous year's harvest of rice was not significantly different between ratoon and main crop. This rice ratooning system requires short duration, creating possibility for growing another crop in the same cropping year and offers an opportunity to increase cropping intensity per unit of cultivated areas.

• Smart Media Journal
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• v.12 no.9
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• pp.28-37
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• 2023

The main factor affecting the decline in rice yield is damage caused by localized heavy rains or typhoons. The method of analyzing the rice lodging area is difficult to obtain objective results based on visual inspection and judgment based on field surveys visiting the affected area. it requires a lot of time and money. In this paper, we propose the method of estimation and diagnosis for rice lodging areas using a Vision Transformer-based Segformer for RGB images, which are captured by unmanned aerial vehicles. The proposed method estimates the lodging, normal, and background area using the Segformer model, and the lodging rate is diagnosed through the rice field inspection criteria in the seed industry Act. The diagnosis result can be used to find the distribution of the rice lodging areas, to show the trend of lodging, and to use the quality management of certified seed in government. The proposed method of rice lodging area estimation shows 98.33% of mean accuracy and 96.79% of mIoU.

• Journal of Crop Science and Biotechnology
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• v.10 no.2
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• pp.86-91
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• 2007

A representative group of 38 improved basmati lines including maintainers of sterile lines were studied for genetic diversity utilizing Mahalanobis $D^2$ statistics. A wide diversity was observed having ten clusters with high intra- and inter-cluster distance. Heterosis was estimated utilizing the cytoplasmic male sterile lines from the clusters having high intra- and inter-cluster distance. Highly heterotic hybrids were obtained from the hybridization programme. Cross combinations IR68281A/Pusa 1235-95-73-1-1, IR68281A/RP 3644-41-9-5, Pusa 3A/UPR 2268-4-1, IR 68281A/Pusa Basmati-1, IR68281A/BTCE 10-98, and IR58025A/HKR 97-401 were found to be highly heterotic for grain yield/plant with other agronomic and quality traits. Additionally, a positive association of intra-cluster distance with heterosis was observed, which could be utilized as a guideline for predicting heterosis in basmati hybrid rice breeding program. Also, a positive association between inter-cluster distance and heterosis was observed.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.