• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• Magazine of the Korean Society of Agricultural Engineers
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• v.17 no.2
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• pp.3772-3777
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• 1975

This paper reports on the analysis of the distributions of probable days being good for mechanical rice harvesting and the method of determining the capacity of rice harvesting machinery for the given harvesting duration. In the analysis of the probability distribution of days being good for rice harvesting, the daily rainfalls above which mechanical field work may be impracticable were specified and their frequency of occurances was analyzed by using the weather records during past twenty-one years measured at five different locations. The conclusions being drawn from the analysis are as follows: 1. The distributions of probable workable days in different region and harvesting duration are very distinct and are different to set a uniform trend (refer to Fig. 1-4). 2. The occurance of probable days being good for mechanical field work under 66% confidence level are quite variable by region and by ten-day period. The analysis indicates that the probable workable days may range from 7.5 to 8.5 days of 10-day span within optimum harvesting duration (refer to Table 1). 3. Based on the probability distributions analyzed, the optimun capacities of harvesting machinery required for different harvesting areas and harvesting start-date were estimated as a function of operating duration (refer to Fig. 5 and Table 2)

• Journal of Biosystems Engineering
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• v.6 no.1
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• pp.83-92
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• 1981

A simulation model of natural air drying to predict the changes of the grain moisture content and dry matter loss of rough rice was developed by the application of mass diffusion theory. A series of simulated drying tests was conducted using the 10 year weather data (1970-1979) obtained from Cheongju, Chuncheon, Daegu, Daejeon, Jeonju, Jinju and Suweon in Korea. System performance factors treated in this study were initial moisture content, airflow rate, bin diameter and grain depth. The results obtained in this study are summarized as follows: 1) The simulation model used in this study was validated with actual experimental results and was applicable to the natural air drying of rough rice. 2) Minimum airflow rates for safe drying were determined for different initial moisture contents and regional weather conditions as shown in Table 6. 3) Equations for estimating drying time and dry matter loss in terms of airflow rate and initial moisture content were derived in the form of an exponential function. 4) These results show that the natural air drying system of rough rice is feasible in Korea even for the poorest drying condition.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.1
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• pp.85-95
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• 1991

• Proceedings of the Korean Society of Crop Science Conference
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• 1990.05a
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• pp.6-8
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• 1990

• Research in Plant Disease
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• v.8 no.4
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• pp.199-206
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• 2002

It was investigated on the relationship of the rice blast epidemics and the real-time meteorological factors, at the experimental paddy field in 1997. Weather factors(temperature, relative humidity, irradiation, precipitation, the direction of wind, wind speed, soil temperature and leaf-wetness, etc) were measured by using the automated weather station. The most influenced weather factor to blast epidemics, was the average max-temp($R^2$= 0.95) during 10 days before leaf blast epidemics, while the least thing was wind speed($R^2$= 0.24). The most potential weather factors correlated with the blast epidemics were T-ave(average temperature), T-max(maximum temperature), RH(Relative Humidity) and RD(Relative Humidity > 90% hrs). A statistics model(the regression equation) of the blast epidemics with the potential weather factors, was established as tallows ; Y = -3410.91 - 23.91 $\times$ T-ave ＋ 28.56 $\times$ T-max ＋ 41.0 $\times$ RH - 3.75 $\times$ RD, ($R^2$= 0.99). (T-ave >= 19$^{\circ}C$, T-max - T-ave >= 5.2$^{\circ}C$ and RH% >= 90.4%). According to the fitness test($\chi$$^2$) of the model, the observed blast disease severity was quite close to those expected.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.31-40
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• 1997

Potential impacts for unfavourable weather conditions and the assessment of the magnitudes of their adverse effects on crop yields were studied. EPIC model was investigated for its capability on crop yield predictions for rice and soybean. Weather generationmodel was used to generate long-term climatic data. The model was verified with ohserved climate data of Suwon city. Fifty years weather data including abnormal conditions were generated and used for crop yield simulation by EPIC model. Crop yield probability function was derived from simulated crop yield data, which followed normal distribution. Probable crop yield reductions due to abnormal weather conditions were also analyzed.

• The Plant Pathology Journal
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• v.38 no.1
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• pp.46-51
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• 2022

Rice blast is the most destructive disease threatening stable rice production in rice-growing areas. Cultivation of disease-resistant rice cultivars is the most effective way to control rice blast disease. However, the rice blast resistance is easy to breakdown within years by blast fungus that continually changes to adapt to new cultivars. Therefore, it is important to continuously monitor the incidence of rice blast disease and race differentiation of rice blast fungus in fields. In 2020, a severe rice blast disease occurred nationwide in Korea. We evaluated the incidence of rice blast disease in Yeoju and compared the weather conditions at the periods of rice blast disease in 2019 and 2020. We investigated the races and avirulence genes of rice blast isolates in Yeoju to identify race diversity and genetic characteristics of the isolates. This study will provide empirical support for rice blast control and the breeding of blast-resistant rice cultivars.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.37-37
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• 2003

Rice is one of the most important crop in the world and is a major export of Thailand. Optical sensors are not useful for rice monitoring, because most cultivated areas are often obscured by cloud during the growing period, especially in South East Asia. Spaceborne Synthetic Aperture Radar (SAR) such as RADARSAT, can see through regardless of weather condition which make it possible to monitor rice growth and to retrieve rice acreage, using the unique temporal signature of rice fields. This paper presents the result of a study of examining the backscatter behavior of rice using multi-temporal RADARSAT dataset. Ground measurements of paddy parameters and water and soil condition were collected. The ground truth information was also used to identify mature rice crops, orchard, road, residence, and aquaculture ponds. Land use class distributions from the RADARSAT image were analyzed. Comparison of the mean DB of each land use class indicated significant differences. Schematic representation of temporal backscatter of rice crop were plotted. Based on the study carried out in Pathum Thani Province test site, the results showed variation of sigma naught from first tillering vegatative phase until ripenning phase. It is suggested that at least, three radar data acquisitions taken at 3 stages of rice growth circle namely; those are at the beginning of rice growth when the field is still covered with water, in the ear differentiation period, and at the beginning of the harvest season, are required for rice monitoring. This pilot project was an experimental one aiming at future operational rice monitoring and potential yield predicttion.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.75-85
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• 2021

This study evaluated variations in the paddy rice water demand based on the continuous changing in rice transplanting period and ponding depth at the four study paddy fields, which represent typical rice producing regions in Korea. Total 7 scenarios on rice transplanting periods were applied while minimum ponding depth of 0 and 20 mm were applied in accordance with maximum ponding depth ranging from 40 mm to 100 mm with 20 mm interval. The weather data from 2013 to 2019 was also considered. The results indicated that the highest rice water demand occurred at high temperature and low rainfall region. Increased rice transplanting periods showed higher rice water demand. The rice water demand for 51 transplanting days closely matched with the actual irrigation water supply. In case of ponding depth, the results showed that the minimum ponding depth had a proportional relationship with rice water demand, while maximum ponding depth showed the contrary results. Minimum ponding depth had a greater impact on rice water demand than the maximum ponding depth. Therefore, these results suggest that increasing the rice transplanting period, which reflects the current practice is desirable for a reliable estimation of rice water demand.