• 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.

• Korean Journal of Environmental Agriculture
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• v.17 no.2
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• pp.122-126
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• 1998

This experiment was conducted to verify the responses of major crop species to saline stress.To determine the saline tolerance of crop species, sevral crop species were cultivated under sand-culture system using Hoagland's nutrient solution whit 200mM NaCl. The crop species showing saline tolerance were cotton(Gossypium indicum), maize(Zea mays), barely(Hordeum vulgare), and wheat(Triticum aestivum) but perilla (Perilla frutescens) and leguminous crops, mung bean(Phaseolus radiatus), azuki bean(Phaseolus angularis) and soy bean(Glycin max) showed very por tolerance. One the typical symptom was the darkening of leaf color due to increase of chlorophyll concentration.Among of the plant families, Fabaceae was the most susceptible but crop species belonging to Poaceae were more proper for cultivating on reclaimed tidal land in the course of desalinazation. It was suggrsted that the crop species belonging to Fabaceae, a sensitive family to soil salinity, must be cultivated when the soil salinity decreased below 10ds/m. To know the critical salinity level for crop growth,salinity of saline soil collected from reclaimed tidal land was adjusted from 10.0ds/m tp41.7ds/m with tap water. It was suggested that the ECs of the soil in which the plant height of each crop spicies was reduced to 50% of control plant were 22.6 and 21.7 ds/m in rice, barley, corn, mung bean, and soy bean,respectively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.141-151
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• 2011

This paper suggests the Facility Management System for plant factory promising to be a core technology of the agriculture in the future. This system makes diagnoses that status from sensors or facilities in the factory for exact operation and monitors the internal environment with the control status in real-time. It is expected that we could operate a plant factory safely and effectively by using the system. The system consists of the data management module, the context provider module, the context interpreter module, the service provider module, the data storage and user interface. The system provide with the failure diagnosis service, the facility control service, and the high-reliability monitoring service via the interactions between above modules. The failure diagnosis service determines whether the sensors or facility devices are in failure or not, and informs the administrator of their conditions. The facility control service is activated in case if the facilities need to be managed during the diagnosis for failure or malfunction processes. The high-reliability monitoring service provides the administrator with verified data through the failure diagnosis service. Then we confirmed that the suggested system operates correctly through the system simulation.

• 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)

• Horticultural Science & Technology
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• v.30 no.3
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• The Journal of Engineering Geology
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• v.26 no.4
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• pp.473-484
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• 2016

This study aims to establish a monitoring method for managing the effective maintenance of tailings dams. The monitoring of a tailings dump area involves several parameters and their investigation through a selection of evaluation items. The extents of defects and progressive failures also need to be effectively estimated. Therefore, the monitoring items can be subdivided into categories relating to the retaining wall structure (concrete wall, reinforcing stone wall, mesh gabions) and general facilities (liner, covering soil, slope, tailings, rain protection facility, leachate, planting), and quantitative evaluations can then be conducted for each condition. In doing so, we developed a systematic monitoring method that assesses the dam maintenance condition with grades and scores. The field application of the monitoring method results showed it to provide a more detailed evaluation than existing monitoring methods: the method detected an additional 16 defects missed by conventional methods. The evaluation gave scores of 89.3, 22.2, and 27.8 to the Geumjang mine tailings dam, the Gupoong mine tailings dam, and the Hwachun mine tailings dam, respectively. The advanced method can provide quantitative evaluation and perform detailed monitoring of the dams. This quantitative evaluation can be used to decide on maintenance priorities, select the main management items, and establish schedules of maintenance.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.667-677
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• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.330-336
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• 2022

It is likely that the heading would occur early when air temperature increases. In 2022, however, the heading date was delayed unusually, e.g., by 3 to 5 days although temperature during the vegetative growth stage was higher than normal years. The objective of this study was to identify the cause of such event analyzing weather variables including average temperature, sunshine hours, and day-length for each growth stage. The observation data were collected for medium-late maturing varieties, which has been grown at crop yield experiment sites including Daegu, Andong, and Yesan. The difference in heading date was compared between growing seasons in 2021 and 2022 because crop management options, e.g., the cultivars and cultivation methods, were identical at those sites during the study period. It appeared that the heading date was delayed due to the difference in temperature responsiveness under a given day-length condition The effect of the temperature increase on the heading date differed between the periods during which when the day-length was more than 14.3 hours before and after the summer-solstice.. The effect of the temperature decrease during the period from which the day-length decreased to less than 14.3 hours to the heading date was relatively greater. This merits further studies to examine the response of rice to the temperature change under different day-length and sunshine duration in terms of heading.