• 한국육종학회지
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• 제43권2호
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• pp.133-138
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• 2011

The groundnut or cultivated peanut (Arachis hypogaea L.) in Korea consists of 36 domestic varieties which have been developed and registered as cultivars for the public during last 25 years. To screen and identify of Korean peanut varieties and genetic resources, we present a simple and reliable method. A methodology based on simple sequence repeat (SSR) markers developed and widely used for prominent gene identification and variety discrimination. For identification of those 36 Korean peanut varieties, 238 unique peanut SSR markers were selected from some previously reported results, synthesized and used for polymerase chain reaction (PCR). Data were taken through acryl amide gel electrophoresis and changed into proper formats for application of data mining analysis using Biomine (all-in-one functional genomics data mining program). Consequently, twelve SSR primers were investigated and revealed the differences between those 36 varieties. These primer pairs amplified 27 alleles with an average of 2.3 allele per primer pair. In addition, those results showed genetic relationship by classification method within 36 varieties. The approach described here could be applied to monitoring of our varieties and adapting to peanut breeding program.

• 한국농림기상학회지
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• 제22권3호
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• pp.176-182
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• 2020

작황조사시험은 우리나라주요 재배지점에 대해 해당 지역에서 주로 재배되는 품종들을 선정하여 장기적으로 벼의 생육과 수량을 관찰하는 연구이다. 이 연구의 목적은 기상 및 병해충의 변화에 따라 재배방법을 능동적으로 수정하여 대응하기 위한 것이다. 장기적인 자료 축적으로 이를 다양한 분야에서도 활용할 수 있도록 이 시험의 이루어지는 장소와 재배관리, 각 장소에 대응하는 기상관측소, 관찰되는 항목들에 대해 소개하였다. 각 관찰항목은 표준적인 절차에 따라 조사되어 일정한 품질이 유지되고 있으며 이 정보들은 현재 문헌으로 모두 공개되어 있으며 전산자료의 형식으로 획득할 수 있다.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• 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.

• 한국농림기상학회지
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• 제22권4호
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• pp.340-345
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• 2020

본 연구에서는 첨단 옥외환경조절시설인 SPAR 시스템의 작물 및 환경 관측 자료의 품질 관리와 보증 방법을 최초로 제시하였다. 특히 실시간 군락 CO2플럭스의 경우에는 수집되는 자료의 특성을 고려하여 이상치의 제거와 보정이 병행되어야 함을 확인하였다. 본 연구를 통해 구축된 자료 처리 방법들은 향후 SPAR 자료를 통한 작물 생육 모형 개선에 매우 중요하게 활용될 수 있을 것으로 보인다. SPAR 내 작물과 환경 관련 10분 평균 자료는 국립식량과학원 내 작물 연구 통합 정보시스템(Crop Research Information System, CRIS) 웹사이트(www2.nics.go.kr/cris)에서 이용 가능하다.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

• 한국농림기상학회지
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• 제22권3호
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• pp.194-204
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• 2020

작물 모형의 품종에 따른 특성을 나타내는 품종 모수를 추정하기 위해서는 많은 양의 생육 관측 자료가 요구되며, 이를 확보하기 위해서는 많은 비용과 노력이 요구된다. 고품질 자료는 아니더라도 공개되어 있는 작물 생육 자료를 활용하여 모수 추정에 사용할 수 있으나, 이러한 자료의 품질에 대한 평가가 선행되어야 한다. 본 연구에서는 농업자료에 대한 정량적 평가 도구인 DatasetRanker를 사용하여 벼에 대한 지역적응시험 자료를 평가하였다. 또한, 결과를 바탕으로 자료의 품질을 개선하기 위한 관측체계의 개선방안을 제시하고자 하였다. 평가 결과 각각의 품종들은 모두 네 등급 중 세 번째로 높은 은 등급으로 평가되었으며, 더 상위의 등급을 얻지 못한 것은 대체로 생육 및 생육환경에 대한 관측자료의 부족에 기인하였다. 결과를 개선하기 위해서는 추가적인 관측자료가 요구되며, 일부 재배관리 등의 기본적인 조건들에 대한 정보를 추가하는 것만으로도 품질에 대한 평가 점수가 약 10%정도 상승할 것으로 예상되었다. 또한, 정확한 위치정보가 공개될 경우 이를 기준으로 수집되는 토양 정보와 기상 정보의 불확실성을 감소시킬 수 있을 것이다. 생육기간 중 시계열적인 관측자료가 수집된다면 품질이 상당히 개선될 것으로 예상되었으며, 이를 위한 연구가 지속적으로 이루어져야 할 것이다.

• Agribusiness and Information Management
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• 제11권1호
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• pp.1-9
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• 2019

This research intends to propose the methodology for analyzing the current trends of agriculture, which directly connects to the survival of the nation, and through this methodology, identify the agricultural trend of Korea. Based on the relationship between three types of data - policy reports, academic articles, and news articles - the research deducts the major issues stored by each data through LDA, the representative topic modeling method. By comparing and analyzing the LDA results deducted from each data source, this study intends to identify the implications regarding the current agricultural trends of Korea. This methodology can be utilized in analyzing industrial trends other than agricultural ones. To go on further, it can also be used as a basic resource for contemplation on potential areas in the future through insight on the current situation. database of the profitability of a total of 180 crop types by analyzing Rural Development Administration's survey of agricultural products income of 115 crop types, small land profitability index survey of 53 crop types, and Statistics Korea's survey of production costs of 12 crop types. Furthermore, this research presents the result and developmental process of a web-based crop introduction decision support system that provides overseas cases of new crop introduction support programs, as well as databases of outstanding business success cases of each crop type researched by agricultural institutions.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• 한국농림기상학회지
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• 제19권4호
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• pp.270-279
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• 2017

최적의 재배관리나 식량생산 관력 정책 수립의 위해 지역적인 작물 생산성 모의 정보들이 사용 될 수 있다. 국내 주요 작물인 벼의 생산성 예측을 위해 ORYZA2000 모델이 널리 사용되어 왔지만, 지역 규모에서 생산성을 예측하기 위한 격자별 작물 모델 구동 체계는 보고되어 있지 않다. 본 연구에서는 격자형식의 입력자료를 사용하여 작물 모델을 구동하고 공간적인 생산성 예측자료를 생산할 수 있는 시스템을 개발하였다. 이를 위해 입출력 처리 모듈과 격자별 모델 구동 모듈을 개발하였으며, 각각의 모듈은 C++와 R을 이용하여 구현되었다. 사례 연구를 위해 남한의 논 지역을 대상으로 2000년대에 대한 생산성을 모의하였다. 1km 및 12.5km 해상도의 격자형 기상자료로부터 13000여개의 기상입력자료가 생성되었다. 관행적인 재배관리 설정을 사용하여 격자별로 구동을 하였으며, 출력자료는 다시 netCDF 형태의 격자형 자료로 취합하였다. 모의된 벼 생산성의 공간적 분포는 실제 분포와 비슷한 경향을 보였으나, 실제 생산성과는 차이가 있었다. 이러한 차이는 이앙시기, 품종 등의 재배관리의 차이 또는 기상자료의 불확실성에 의해 생기게 된다. 본 연구에서 개발된 격자별 모델 구동 시스템을 통해 다른 작물 모델을 이용한 격자별 모의가 가능할 것이다.