• 한국농림기상학회지
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• 제22권3호
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• pp.135-143
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• 2020

단파 복사와 일조시간은 농작물 재배에 중요한 변수들이다. 그러나 국내에서 제공되는 일사 관측 자료는 수평 해상도가 높지 않아 농업 현장에 활용하기 어렵다. 본 연구에서는 지면대기모델링패키지(LAMP)를 이용하여 시간단위 일사 자료를 물리역학적으로 생산하고, 통계적 다운스케일링을 통해 고해상도 일단위 기상기후 DB를 구축하였다. 현재 이 DB는 품질 평가를 거쳐 농업가뭄 재해와 밭작물의 생육 현황을 진단하고 예측하는 '경기도 농업가뭄 예측시스템'의 공식 빅데이터 입력 자료로 활용되고 있다.

• 한국농림기상학회지
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• 제22권3호
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• pp.171-175
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• 2020

대기오염의 사회경제적 효과에 대한 연구에는 측정된 대기오염 물질, 기상 자료, 그리고 사회경제적 데이터의 병합이 필요하다. 이들 자료들의 시간적·공간적 범위와 단위가 상이하기 때문에 분석에 필요한 데이터 가공에 많은 시간과 노력이 요구된다. 본 데이터의 구축은 사회과학 분야에서 널리 사용되는 대표적인 대기오염 및 기상 변수를 시군구 단위로 제공하는 것을 목표로 한다. 2020년 8월 기준 배포 버전 데이터의 시간적 범위는 2001년부터 2018년이며, 공간적 범위는 250개 시군구로서 패널 형태의 자료를 제공한다. 본 데이터의 기상 변수들은 대기오염 관련 분석뿐만 아니라 다양한 사회과학의 연구에서 사용할 수 있는 주요 변수들을 포함하고 있다.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2016년도 추계 학술발표논문집
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• pp.143-146
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• 2016

• 한국컴퓨터정보학회논문지
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• 제27권2호
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• pp.171-177
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• 2022

본 논문에서는 다출처 농업 데이터를 통찰할 수 있는 지식체계를 마련하고, 시간 흐름을 가지는 환경인자 분석 정보를 클러스터링 할 수 있는, 농작물 환경 인자 큐레이션 서비스 방법을 제안한다. 제안하는 큐레이션 서비스는 크게 수집, 전처리, 저장, 분석의 네 단계로 구성된다. 첫째, 수집 단계에서는 OpenAPI 기반의 웹크롤러를 이용하여 다출처 농업 데이터에 대한 수집 및 정리를 수행한다. 둘째, 전처리 단계에서는 데이터 측정 오차를 감소시키기 위해 데이터 평활화를 수행한다. 이때 온실, 노지 등의 시설 특성에 따른 오차율을 고려하여 시설 유형별 평활화 방법을 적용한다. 셋째, 저장단계에서는 대용량 농업 데이터 관리를 위해, 농업 데이터 통합 스키마 및 Hadoop HDFS 기반의 저장 구조를 제안한다. 마지막으로 분석 단계에서는 농업 디지털 데이터의 시계열 특성을 고려한 DTW 기반의 시계열 분류를 수행한다. DTW 기반 시계열 분류를 통해 시계열 데이터의 특성을 손실 없이 반영하여 예측 결과 정확도를 향상시킨다. 향후 연구로는 제안한 서비스 방법을 구현하여 스마트팜 온실에 적용하고, 테스트 및 검증을 수행할 예정이다.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2016년도 추계 학술발표논문집
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• pp.1-5
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• 2016

• 유통과학연구
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• 제20권11호
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• pp.79-88
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• 2022

Purpose: The purpose of this study was to determine the effect of agricultural exports and imports on economic growth in Bangladesh and propose an upgraded and customized model of the supply chain for agribusiness growth in Bangladesh to achieve plain sailing and systematic operation and financial gains at home and abroad. Research design, data, and methodology: All data in the research have been collected from secondary sources. Gross domestic product was used as the dependent variable and exports and imports of agricultural products were used as independent variables. Pairwise Granger causality was utilized to see the impact of the variable responsible for the economic growth in Bangladesh and the causal relationship between the variables analyzed was measured using Johansen co-integration test. Results: From the empirical analysis, the researchers observed that agricultural commodity imports and exports have a unidirectional impact on economic growth in Bangladesh and a long-run causal link with economic growth in Bangladesh. The suggested supply chain model of agribusiness aids in achieving smooth operations, systematic management, and monetary gains both domestically and internationally. Conclusions: This paper contributes to the development of a more effective and profitable agribusiness supply chain in Bangladesh systematically through their theoretical and practical implications.

• 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.

• 한국환경농학회지
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• 제38권3호
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• pp.159-165
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• 2019

BACKGROUND: The water footprint (WF) is an indicator of freshwater use that appears not only at direct water use of a consumer or producer, but also at the indirect water use. As an indicator of 'water use', the water footprint includes the green, blue, and grey WF, and differs from the classical measure of 'water withdrawal' because of green and grey WF. This study was conducted to assess and estimate the water footprint of the soybean and Chinese cabbage. METHODS AND RESULTS: APEX model with weather data, soil and water quality data from NAS (National Institute of Agricultural Sciences), and farming data from RDA (Rural Development Administration) was operated for analyzing the WF of the crops. As the result of comparing the yield estimated from APEX with the yield extracted from statistic data of each county, the coefficients of determination were 0.83 for soybean and 0.97 for Chinese cabbage and p-value was statistically significant. The WFs of the soybean and Chinese cabbage at production procedure were 1,985 L/Kg and 58 L/Kg, respectively. This difference may have originated from the cultivation duration. The WF ratios of soybean were 91.1% for green WF and 8.9% for grey WF, but the WF ratios of Chinese cabbage were 41.5% for green WF and 58.5% for grey WF. CONCLUSION: These results mean that the efficiency of water use for soybean is better than that for Chinese cabbage. The results could also be useful as an information to assess environmental impact of water use and agricultural farming on soybean and Chinese cabbage.

• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.46-53
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• 2022

RIn this study, a test tractor that could measure various types of agricultural operational loads was developed, and its performance was verified. This tractor could be used to measure the load generated during agricultural work and convert the related data into a database. A test tractor was developed using an 80-kW-rated load tractor, and it could measure various types of field test data, such as engine torque and rpm, wheel torque, PTO(power take-off) torque, hexometer, IMU/INS sensor, steering angle sensor, hydraulic pressure, and flow sensor data. To verify the developed test tractor, a verification test using an agriculture rotavator was performed. The test conditions were L1, L2, and L3 based on the tractor's main and sub-transmission stages, and stages 1 and 2 were selected as the PTO. In a comparison of the analyzed test data, similar tendencies in the test results of this research and other research (Kim's research) were seen. Through this, the developed test tractor was verified. In the future, we plan to conduct research on the tractor developed in this study using various attached working machines.

• 대기
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• 제34권1호
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• pp.69-81
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• 2024

In this paper, Annual flux variations in the Boseong Tall Tower (BTT) from 2016 to 2020 were analyzed using data from three levels (2.5 m, 60 m, and 300 m). BTT was installed in Boseong-gun, Jeollanam-do in February 2014 and continued to conduct energy exchange observations such as CO2, sensible heat, and latent heat using the eddy covariance method until March 2023. The BTT was located in a very flat and uniform paddy field, and flux observations were conducted at four levels: 2.5 m, 60 m, 140 m, and 300 m above ground. Surface energy balance was confirmed from observed data of net radiation flux, soil heat flux, sensible heat flux, and latent heat flux. Additionally, 2.5 m height surface fluxes, which are most influenced by agricultural land, were compared with data from Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration to evaluate the accuracy of LDAPS flux data. The correlation coefficient between LDAPS flux data and observed values was 0.95 or higher. Excluding summer latent heat flux data, there was a general tendency for LDAPS data to be higher than observed values. The footprint areas estimated below 60 m height mainly covered agricultural land, and flux observations at 2.5 m and 60 m heights showed typical agricultural characteristics. In contrast, the footprint estimated at 300 m height did not show agricultural characteristics, indicating that observations at this height encompassed a wide range, including mountains, sea, and roads. The analysis results of long-term flux observations can contribute to understanding the energy and carbon dioxide fluxes in agricultural fields. Furthermore, these results can be utilized as essential data for validating and improving numerical models related to such fluxes.