• Korean Journal of Breeding Science
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• v.43 no.2
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.176-182
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• 2020

National Institute of crop Science of the Rural Development Administration (RDA) has conducted long-term monitoring studies to find out the relationship between crop yield and climatic factors for major food crops including rice. Rice growth and y ield have been monitored in 17 regions where the branches of the National Institute of Crop science and the Provincial Agricultural Research and Extension Service locate. The data obtained from monitoring studies for rice growth and yield include the observation of vegetative growth status and yield components, which include leaf number, biomass and the weight of 1000 grains. These data have been collected from rice fields where standard management procedures have been applied. The observation data for crop growth and yield monitoring studies from 1999 to 2019 are open to public through agricultural science library operated by RDA.

• Journal of Information Processing Systems
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• v.20 no.2
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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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• v.14 no.3
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.340-345
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• 2020

In this study, we developed the quality control and assurance method of measurement data of SPAR (Soil-Plant-Atmosphere-Research) system, a climate change research facility, for the first time. It was found that the precise processing of CO2 flux data among many observations were sig nificantly important to increase the accuracy of canopy photosynthesis measurements in the SPAR system. The collected raw CO2 flux data should first be removed error and missing data and then replaced with estimated data according to photosynthetic lig ht response curve model. Comparing the correlation between cumulative net assimilation and soybean biomass, the quality control and assurance of the raw CO2 flux data showed an improved effect on canopy photosynthesis evaluation by increasing the coefficient of determination (R2) and lowering the root mean square error (RMSE). These data processing methods are expected to be usefully applied to the development of crop growth model using SPAR system.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.194-204
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• 2020

Cultivar parameters, which are key inputs to a crop growth model, have been estimated using observation data in good quality. Observation data with high quality often require considerable labor and cost, which makes it challenging to gather a large quantity of data for calibration of cultivar parameters. Alternatively, data in sufficient quantity can be collected from the reports on the evaluation of cultivars by region although these data are of questionable quality. The objective of our study was to assess the quality of crop and management data available from the reports on the regional adaptation trials for rice cultivars. We also aimed to propose the measures for improvement of the data quality, which would aid reliable estimation of cultivar parameters. DatasetRanker, which is the tool designed for quantitative assessment of the data for parameter calibration, was used to evaluate the quality of the data available from the regional adaptation trials. It was found that these data for rice cultivars were classified into the Silver class, which could be used for validation or calibration of key cultivar parameters. However, those regional adaptation trial data would fall short of the quality for model improvement. Additional information on management, e.g., harvest and irrigation management, can increase the quantitative quality by 10% with the minimum effort and cost. The quality of the data can also be improved through measurements of initial conditions for crop growth simulations such as soil moisture and nutrients. In addition, crop model improvement can be facilitated using crop growth data in time series, which merits further studies on development of approaches for non-destructive methods to monitor the crop growth.

• Agribusiness and Information Management
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• v.11 no.1
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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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.270-279
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• 2017

Regional assessment of crop productivity using a gridded simulation approach could aid policy making and crop management. Still, little effort has been made to develop the systems that allows gridded simulations of crop growth using ORYZA 2000 model, which has been used for predicting rice yield in Korea. The objectives of this study were to develop a series of data processing modules for creating input data files, running the crop model, and aggregating output files in a region of interest using gridded data files. These modules were implemented using C++ and R to make the best use of the features provided by these programming languages. In a case study, 13000 input files in a plain text format were prepared using daily gridded weather data that had spatial resolution of 1km and 12.5 km for the period of 2001-2010. Using the text files as inputs to ORYZA2000 model, crop yield simulations were performed for each grid cell using a scenario of crop management practices. After output files were created for grid cells that represent a paddy rice field in South Korea, each output file was aggregated into an output file in the netCDF format. It was found that the spatial pattern of crop yield was relatively similar to actual distribution of yields in Korea, although there were biases of crop yield depending on regions. It seemed that those differences resulted from uncertainties incurred in input data, e.g., transplanting date, cultivar in an area, as well as weather data. Our results indicated that a set of tools developed in this study would be useful for gridded simulation of different crop models. In the further study, it would be worthwhile to take into account compatibility to a modeling interface library for integrated simulation of an agricultural ecosystem.