• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.183-183
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• 2017

In the early part of rice growth, root volume primarily limits the amount of plant-accessible nitrogen (N). Therefore, knowledge of the root development is important for modeling N uptake of rice. The timing when the volume of rhizosphere cover the whole soil is also important to carry out timely top dressing. However, information about initial root expansion and associated N uptake is limited due to intrinsic technical difficulties in assessing below-ground processes. Some studies, however, showed a close relationship between below-ground root and above-ground leaf development, suggesting a possibility that above-ground attributes could serve as surrogates for the root processes. In this study, we investigated the relationship between below-ground and above-ground development of rice. Field experiments were conducted where we cultivated Koshihikari (a leading cultivar in Japan) for four different cropping schedules in 2012. In 2016, Gimbozu (HEG4) and three flowering time mutant lines of Gimbozu (X61 (se13), HS276 (ef7), DMG9 (se13, ef7)) were examined for a single season. Experiments were performed with three replications in a completely randomized design. We monitored ammonium-N concentration ([NH4+-N]) in soil solution by repeatedly taking samples from a porous tubing (10-cm long) vertically inserted at the most distant point from surrounding rice hills. Samples were taken in triplicate (= triplicate tubes) and every three days from transplanting in each experimental unit. For above-ground attributes, leaf area index (LAI) was measured in 2012, whereas soil coverage ratio was estimated by image processing in 2016. Results showed that [NH4+-N] increased gradually after transplanting and then rapidly decreased from a certain day. This distinct drop in [NH4+-N] informed us the timing at which the rice root system reached the point of porous tubing and thus essentially covered the whole soil volume. The LAI at the dropping point was about 0.43 regardless of the cropping schedules in 2012 experiment. In 2016, the coverage ratio at the N dropping point was within the range of 0.12 to 0.19 for four genotypes having different growth durations. In addition, the coverage ratios at seven weeks after the transplanting showed a good correspondence to LAI across the four genotypes. We therefore conclude that both LAI and coverage ratio may serve as robust indicators for root development and might be useful to estimate the timing when the root system fully cover the soil volume. Results obtained here will also contribute to develop models that can predict not only above-ground canopy development but also associated below-ground processes.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.382-387
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• 1992

In order to obtain desired arc welding performance, we already developed an arc welding robot system that enabled coordinated motions of dual arm robots. In this system one robot arm holds a welding target as a positioning device, and the other robot moves the welding torch. Concerning to such a dual arm robot system, the positioning accuracy of robots is one important problem, since nowadays conventional industrial robots unfortunately don't have enough absolute accuracy in position. In order to cope with this problem, our robot system employed teaching playback method, where absolute error are compensated by the operator's visual feedback. Due to this system, an ideal arc welding considering the posture of the welding target and the directions of the gravity has become possible. Another problem still remains, while we developed an original teaching method of the dual arm robots with coordinated motions. The problem is that manual teaching tasks are still tedious since they need fine movements with intensive attentions. Therefore, we developed a 3-dimensional vision guided robot control method for our welding robot system with coordinated motions. In this paper we show our 3-dimensional vision sensor to guide our arc welding robot system with coordinated motions. A sensing device is compactly designed and is mounted on the tip of the arc welding robot. The sensor detects the 3-dimensional shape of groove on the target work which needs to be weld. And the welding robot is controlled to trace the grooves with accuracy. The principle of the 3-dimensional measurement is depend on the slit-ray projection method. In order to realize a slit-ray projection method, two laser slit-ray projectors and one CCD TV camera are compactly mounted. Tactful image processing enabled 3-dimensional data processing without suffering from disturbance lights. The 3-dimensional information of the target groove is combined with the rough teaching data they are given by the operator in advance. Therefore, the teaching tasks are simplified

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.347-352
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• 2016

Recently, IoT technology has been applied in various field. In particular, the technology focuses on analysing large amount of data that has been gathered from the environmental sensors, to provide valuable information. This technique has been actively researched in the agro-industrial sector. Many researches are underway in the monitoring and control for growth crop environment in agro-industrial. Normally, the average weather data is provided by the manual agro-control method but the value may differ due to the different region's weather and environment that may cause problem in the disease and insect-pest prevention. In order to develop a suitable integrated system for fruit tree, all the necessary information is obtained from the Jeollanam-do province, which has the high production rate in the Korea. In this paper, we propose an integrated support platform for the growing crops, to minimize the damage caused due to the weather disaster through image analysis, forecasting models, by using the micro-climate weather information collection and CCTV. The fruit tree damage caused by the weather disaster are controlled by utilizing various IoT technology by maintaining the growth environment, which helps in the disease and insect-pest prevention and also helps farmers to improve the expected production.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.465-474
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• 2009

To measure the amount of nonpoint source pollution, some efforts are tried to utilize satellite imagery. But, as the factors for water models do not relate with the landcover categories for satellite imagery, satellite imagery are adapted to roughly classified thematic map or used only for the image interpretation. The purpose of this study is to establish the landcover categories of satellite imagery to relate with the water models. To establish the categories of the landcover for the water models, it was investigated to get main factors of water flow models for the nonpoint source pollution and to review the existing study and the classification system. For this result, it was convinced that the basic unit on the nonpoint source pollution, landcover coefficients of SCS Curve Number, the crop factor of Universal Soil Loss Equation, Manning's roughness coefficients are the useful parameters to extract information from the satellite imagery. After the setup the categories for the landcover classification, it was finally defined from the consultation of the water model specialist. Woopo wetland watershed was selected to the study area because it is a representative wetland in Korea and needs the management system for nonpoint source pollution. There were used Landsat ETM Plus and SPOT-5 satellite imagery to assess the result of the image classification.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.