• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2000년도 춘계임시총회 및 학술발표대회
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• pp.26-33
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• 2000

This study was conducted to investigate the relationships between yearly variations of elimatic elements and yearly variations of productivity in potato. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 9 years from 1987 to 1995. The meteorological data what gathered at the Goheung Weather Station for the same period of crop growing season were used to find out the relationships between climatic elements and crop productivity. Yearly variation of the daily minimum temperature in March and April were large with coefficients of variation (C.V.) of 126.0%, 368%, respectively, but the variation of the daily mean and maximum temperature in May and June were relative small. Stem length and number of stem show more C.V. of 9.3%, 14.3%, respectively, but the variation of the yield was relative small with 3.7%. Correlation coefficients between the amount of precipitation in April and yield, yield and daily mean temperature in June were negatively significant at the level of 5, 1 %, respectively. Correlation coefficients between the growth habits and yield are positively significant at the level of 5, 1 %, respectively. Simple linear regression equations by the least square method are estimated for stem length (Yl) and the precipitation in April(X) as Y,=82.47-0.11x (R2=0.3959), and for yield(Y2) and the precipitation in April(X) as Y,=2003.61-0.94X (R2=0.5418).

• 한국측량학회지
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• 제40권5호
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• pp.449-456
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• 2022

본 연구는 GIS를 통해 토양정보를 수집하고 가공하여 농산물 생산량을 예측하는 모델을 제안한다. 농산물 생산량 예측 딥러닝 알고리즘은 공개된 CNN-RNN 농산물 생산량 예측 모델 구조를 변경하여 국내 농산물 자료 환경에 적합하도록 새롭게 구축하였다. 기존모델은 두 가지 특징을 가지고 있는데 첫 번째는 농산물의 생산량을 해당 필지값이 아닌 당해 평균값으로 대체한다는 것이고 두 번째는 예측하는 연도의 데이터까지 학습한다는 것이다. 새로운 모델은 해당 필지의 값을 그대로 사용하여 데이터의 정확성을 확보하고 예측하고자 하는 연도 이전의 데이터만 가지고 학습할 수 있도록 네트워크 구조를 개선하였다. 제안한 CNN-RNN 모델은 1980년부터 2020년까지의 기상정보, 토양정보, 토양적성도, 생산량 데이터를 학습하여 김장용 가을배추의 지역별 단위면적당 생산량을 예측한다. 2018년부터 2021년까지 4개 연도별 자료에 대하여 계산하고 생산량을 예측한 결과, 테스트 데이터셋에 대한 오차백분율이 약 10% 내외로 실제값과 비교하여 정확도 높은 생산량 예측이 가능했고, 특히 전체 생산량 비중이 큰 지역에서의 생산량은 비교적 근접하게 예측하는 것으로 분석되었다. 또한 제안모델과 기존모델은 모두 학습자료 연도 수가 증가할수록 점점 오차가 작아지므로 학습데이터가 많아질수록 범용 성능은 향상되는 결과를 나타낸다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2333-2337
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• 2007

The practical studies on the method of particle contamination control for yield enhancement in the cleanroom were carried out. The method of the contamination control was considered, which is composed of data collection, data analysis, improvement action, verification, and implement control. The composition analysis for data collection and data analysis was used in the cellular phone module packaging lines. And this method was evaluated by the variation of yield loss between before and after improvement action. In case that the composition analysis was applied, the critical sources were selected and yield loss reduction through improvement actions was also investigated. From these results, it is concluded that the composition analysis is effective solutions for particle contamination control in the cleanroom.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2338-2343
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• 2007

The practical studies on the method of particle contamination control for yield enhancement in the cleanroom were carried out. The method of the contamination control was proposed, which are composed of data collection, data analysis, improvement action, verification, and implement control. The partition check method for data collection and data analysis was used in the cellular phone module production lines. And this method was evaluated by the variation of yield loss between before and after improvement action. In case that the partition check method was applied, the critical process step was selected and yield loss reduction through improvement actions was observed. From these results, it is concluded that the partition check method is effective solution for particle contamination control in the cleanroom production lines.

• Plant Resources
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• 제6권3호
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• pp.178-182
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• 2003

This study was conducted to investigate the relationship between yearly variations of climatic elements and yearly variations of productivity in perilla. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1991 to 2000. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly variation of the amount of precipitation in September was large with coefficients of variation(c. v.) of 11.1%, but the coefficient of variance(c. v.) in July and August were relative small with 1.8, 2.1%, respectively. Number of cluster per hill and weight of 1,000 grains were greatly with c. v. of 76.1, 79.3%, respectively, but the coefficients of variance(c. v.) of plant height and seed yield were more less with 9.58, 10.60%, respectively. Correlation coefficients between precipitation of September and seed yield were positively significant correlation at the level of 5.1%, respectively, but the duration of sunshine in September and seed yield were negatively significant at the level of 5.1%, respectively. Correlation coefficients of these, the plant height, number of branches per plant, cluster length, number of cluster per hill, weight of 1,000 grains and seed yield were positively significant at the level of 5.1% respectively.

• 한국연초학회지
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• 제3권2호
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• pp.115-122
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• 1981

Data for this study were obtained from Burley 21 (Nicotiana tabacum L.) grown under various densities on the field in 1978 and 1979 at the Jeonju Experiment Station, Korea Ginseng & Tobacco Research Institute. Interrelations between yield and its components were statistically studied by correlation, regression, and pathway analysis. Correlation of yield with plant population was significant and positive. Quadratic functions for yield vs. plant population and the length of the largest leaf were fitted to the data. Multiple recession equation between yield and its components (leaf number ($X_1$), a leaf area ($X_5$), weight per unit leaf area ($X_9$), plant population ($X_14$)), was significant at the 5% level. Measuring the relative importance of its components on yield, plant population was 49.5%, weight per unit leaf area 25.3%, a leaf 15.6%, and leaf number 9.8%.

• Journal of Biosystems Engineering
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• 제24권2호
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• pp.135-144
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• 1999

An yield monitoring system plays a key role in precision farming. An yield monitoring system and a DGPS were implemented to a widely used domestic combine for yield mapping of a small sized paddy field, and yield mapping algorithms were investigated in this study. The yield variation in the 0.1ha rice paddy field was measured by installing a yield flow sensor and a grain moisture sensor at the end of the clean grain elevator discharging grains into a grain tank. Yield map of the test filed was drawn in a point map and a linear interpolated map based on the result of the field test. The size of a unit yield grid in yield mapping was determined based on the combine traveling speed, effective harvesting width and data storing period. It was possible to construct the yield map of a small sized paddy field.

• 대한원격탐사학회지
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• 제37권2호
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• pp.199-209
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• 2021

장기적인 시계열 수량 평균이 기술적인 발전 요인에 의해 증가하는 추세를 제거하여, 기존 MODIS NDVI 및 기상자료를 이용한 우리나라 벼 수량 추정 모형을 개선하고자 하였다. 이를 위해 2002년부터 2019년 까지의 NDVI (MYD13Q1)와 기상자료를 사용하여 다중 선형 회귀 분석을 수행하였다. 벼 수량 추세를 분석하고 이를 제거하여 모형을 보완하였다. 개선된 모형을 이용하여 추정한 벼 수량과 수량 통계 값 간의 상관 분석을 통해 추세 제거에 따른 정확도를 평가하였다. 그 결과, 추세가 제거된 벼 수량 추정 모형에 의해 예측된 수량이 통계 수량의 연간 변동 특성을 잘 반영하고 있는 것으로 나타났다. 추세 제거 전의 모형과 비교하여 통계 수량과의 상관계수와 결정계수도 높게 나타났다. 따라서 추세 제거 방법이 벼 수량 추정 모형을 효과적으로 보정하는 방법임을 확인하였다.

• Advances in nano research
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• 제14권3호
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• pp.225-234
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• 2023

In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.

• Plant Resources
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• 제6권3호
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• pp.159-164
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• 2003

This study was conducted to investigate the relationship between yearly variations of climatic elements and yearly variations of productivity in malting barley. In addition, correlation coefficients among yield and yield components were estimated. The data of yield and yield components were investigated for 10 years from 1991 to 2000. The meteorological data gathered at the Yeosu Weather Station for the same period were used to find out the relationships between climatic elements and productivity. Yearly varation of the amount of precipitation in December and January were large with coefficients of variation(c. v.) of 97.9, 51.3%, respectively, but the variation of the maximum temperature and minimum temperature in April were relative small. Yield, weight of 1,000 grains and culm length were greatly with c. v. of 37.3, 49.3 and 41.3%, respectively. spike length and number of spikes show more or less c. v. of 3.8, 24.7% respectively and number of grains per spike show still less variation with c. v. of 9.4%. Correlation coefficients between temperature of mean, maximum and minimum in February and seed yield and yield components were positively significant at level of 5.1%, respectively. Correlation coefficients between precipitation of April and seed yield were positively significant correlation at the level of 5.1 %, respectively, but the duration of sunshine in April and seed yield were negatively significant at the level of 5.1%, respectively. Correlation coefficients of those, yield components and yield, culm length, spike length, number of grains per spike, number of spikes per $m^2$, weight of 1,000 grains and seed yield were positively significant at the level of 5.1 % respectively.