• Proceedings of the Korean Society of Crop Science Conference
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• 2022.04a
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• pp.110-110
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• 2022

Rice is the world's most important food crop and a major source of nutrition for about two thirds of populations. Northern Vietnam is one of the most important centers of genetic diversity for cultivated rice. In this study, we determined the genetic diversity and population structure of 79 rice landraces collected from northern Vietnam and 19 rice accessions collected from different countries. In total, 98 rice accessions could be differentiated into japonica and indica with moderate genetic diversity and a polymorphism information content of 0.382. We also detected subspecies-specific markers to classify rice (Oryza sativa L.) into indica and japonica. Additionally, we detected five marker-trait associations and rare alleles that can be applied in future breeding programs. Most interestingly, analysis of molecular variance (AMOVA) found genetic differentiation was related to geographical regions with an overall PhiPT (analog of fixation index FST) value of 0.130. More emphasis was given to provide signatures and infer explanations about the role of geographical isolation and environmental heterogeneity in genetic differentiation among regions in landraces from northern Vietnam. Our results suggest that rice landraces in northern Vietnam have a dynamic genetic system that can create different levels of genetic differentiation among regions, but also maintain a balanced genetic diversity between regions.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• Journal of Korean Society of Forest Science
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• v.95 no.2
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• pp.177-182
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• 2006

Little information is available on soil $CO_2$ efflux and crop yield under agroforestry systems. Soil $CO_2$ efflux, microbial biomass C, live fine root biomass, and cotton yield were measured under a pecan (Carya illinoinensis K. Koch)-cotton (Gossypium hirsutum L.) alley cropping system in southern USA. A belowground polyethylene root barrier was used to isolate tree roots from cotton which is to provide barrier and non-barrier treatments. The barrier and non-barrier treatment was randomly divided into three plots for conventional inorganic fertilizer application and the other three plots for organic poultry litter application. The rate of soil $CO_2$ efflux and the soil microbial biomass C were affected significantly (P < 0.05) by the fertilizer treatment while no significant effect of the barrier treatment was occurred. Cotton lint yield was significantly (P < 0.0 I) affected by the root barrier treatment while no effect was occurred by the fertilizer treatment with the yields being greatest ($521.2kg\;ha^{-1}$) in the root barrier ${\times}$ inorganic fertilizer treatment and lowest ($159.8kg\;ha^{-1}$) in the non-barrier ${\times}$ inorganic fertilizer treatment. The results suggest that the separation of tree-crop root systems with the application of inorganic fertilizer influence the soil moisture and soil N availability, which in tum will affect the magnitude of crop yield.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.63-71
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• 2010

In this paper, we propose the Agricultural Environment Monitoring System based on Sensor Network which can collect information of crop cultivation environment and monitor it in real-time by using various environment sensors. Existing wireless sensor nodes, based on the sensor network, require extra conversion/control module depending on the characteristics. To solve this problem, we developed an integrated sensor module which can integrate various kinds of sensors used to obtain the necessary information for the area under crop cultivation. In addition, we developed sensor networks monitoring system which is suitable for an integrated sensor module. To verify the operating status of the proposed system, an integrated sensor node is installed in the test environment so that it can sense information of the environment and monitor it in real-time.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.91-97
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• 2018

The plant factory represents one of the future agricultural systems into which ubiquitous information technology (U-IT) is incorporated, including sensor networking, and helps minimize the influence of external experimental factors that constrain the use of existing greenhouse cultivation techniques. A plant factory's automated cultivation system does not merely provide convenience for crop cultivation, but also expandability as a platform that helps build a knowledge database based on its acquired information and develop education and other application services using the database. For the expansion of plant factory services, this study designed a plant factory interworking service (PFIS) which allows plant factories to share crop growth-related information efficiently among them and performed a test on the service and its implementation.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.699-717
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• 2021

South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.2
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• pp.109-115
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• 2006

Understanding characteristics of claypan soils has long been an issue for researchers and farmers because the high-clay subsoil has a pronounced effect on grain crop productivity. The claypan restricts water infiltration and storage within the crop root zone, but these effects are not uniform within fields. Conventional techniques of identifying claypan soil characteristics require manual probing and analysis which can be quite expensive; an expense most farmers are unwilling to pay. On the other hand, farmers would be very interested if this information could be obtained with easy-to-use field sensors. Two examples of sensors that show promise for helping in claypan soil characterization are soil profile strength sensing and bulk soil apparent electrical conductivity (ECa). Little has been reported on claypan soils relating the combined information from these two sensors with grain crop yield. The objective of this research was to identify the relationships of sensed profile soil strength and soil EC with nine years of crop yield (maize and soybean) from a claypan soil field in central Missouri. A multiple-probe (five probes on 19-cm spacing) cone penetrometer was used to measure soil strength and an electromagnetic induction sensor was used to measure soil EC at 55 grid site locations within a 4-ha research field. Crop yields were obtained using a combine equipped with a yield monitoring system. Soil strength at the 15 to 45 cm soil depth were significantly correlated to crop yield and ECa. Estimated crop yields from apparent electrical conductivity and soil strength were validated with an independent data set. Using measurements from these two sensors, standard error rates for estimating yield ranged from 9 to 16%. In conclusion, these results showed that the sensed profile soil strength and soil EC could be used as a measure of the soil productivity for grain crop production.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.758-760
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• 2022

본 연구는 머신러닝의 한 종류인 YOLOv5를 이용하여 기존 육안 선별작업을 자동화 하는 기계를 설계하는 것이다. 본 연구에서는 영상촬영과 선별작업을 진행하는 컨베이어 기구와 선별 프로그램을 제작하고, 모든 표면을 검사해 사과의 품질을 3단계로 구별하는 작업을 진행하였다. 결과적으로 투입된 사과의 품질을 성공적으로 분류 하였다.