• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.224-224
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• 2019

• Journal of Korean Society of Forest Science
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• v.108 no.2
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• pp.208-215
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• 2019

The goal of this study was to analyze the reactivity of a volumetric water content sensor (soil moisture sensor) and tensiometer and to review their use in the early detection of a shallow landslide. We attempted to demonstrate shallow and rapid slope collapses using three different soil ratios under artificial rainfall at 120 mm/h. Our results showed that the measured value of the volumetric water-content sensor converged to 30~37%, and that of the tensiometer reached -3~-5 kPa immediately before the collapse of the soil under all three conditions. Based on these results, we discussed a temporal range for early warnings of landslides using measurements of the volumetric water content sensors installed at the bottom of the soil slope, but could not generalize and clarify the exact timing for these early warnings. Further experiments under various conditions are needed to determine how to use both sensors for the early detection of shallow landslides.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1049-1051
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• 2003

This study investigated the use of airborne image data to provide estimates of within -field variability in soil properties and crop growth as an alternative to extensive field data collection. Hyperspectral and multispectral images were acquired in 2000, 2001, and 2002 for central Missouri experimental fields. Data were converted to reflectance using chemically-treated reference tarps with known reflectance levels. Geometric distortion of the hyperspectral pushbroom sensor images was corrected with a rubber sheeting transformation. Statistical analyses were used to relate image data to field-measured soil properties and crop characteristics. Results showed that this approach has potential; however, it is important to address a number of implementation issues to insure quality data and accurate interpretations.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.33-39
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• 2011

An optical sensing approach based on diffuse reflectance has shown potential for rapid and reliable on-site estimation of soil properties. Important sensing ranges and the resulting regression models useful for soil property estimation have been reported. In this study, a similar approach was applied to investigate the potential of reflectance sensing in estimating soil properties for Korean paddy fields. Soil cores up to a 65-cm depth were collected from 42 paddy fields representing 14 distinct soil series that account for 74% of the total Korean paddy field area. These were analyzed in the laboratory for several important physical and chemical properties. Using air-dried, sieved soil samples, reflectance data were obtained from 350 to 2500 nm on a 3 nm sampling interval with a laboratory spectrometer. Calibrations were developed using partial least squares (PLS) regression, and wavelength bands important for estimating the measured soil properties were identified. PLS regression provided good estimations of Mg ($R^2$ = 0.80), Ca ($R^2$ = 0.77), and total C ($R^2$ = 0.92); fair estimations of pH, EC, $P_2O_5$, K, Na, sand, silt, and clay ($R^2$ = 0.59 to 0.72); and poor estimation of total N. Many wavelengths selected for estimation of the soil properties were identical or similar for multiple soil properties. More important wavelengths were selected in the visible-short NIR range (350-1000 nm) and the long NIR range (1800-2500 nm) than in the intermediate NIR range (1000-1800 nm). These results will be useful for design and application of in-situ close range sensors for paddy field soil properties.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.27-35
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• 2007

The majority of farm managers growing the garden products in greenhouse concern massively about the diagnosis and prevention of the breeding and extermination for pests. especially, the managing problem for pests turns up as main issue. In the paper, we first build a wireless sensor network with soil and environment sensors such as illumination, temperature and humidity. And then we design and implement multimedia pest predication and management system which is able to predict and manage various pest of garden products in greenhouse. The proposed system can support the database with information about the pests by building up wireless sensor network in greenhouse compared with existing high-priced PLC device as well as collect various environment information from soil, the interior of greenhouse, and the exterior of greenhouse. To verify the good capability of our system, we implemented several GUI interface corresponding desktop. web, and PDA mobile platform based on real greenhouse model. Finally, we can confirm that our system work well prediction and management of pest of garden products in greenhouse based on several platforms.

• Journal of Soil and Groundwater Environment
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• v.29 no.4
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• pp.1-11
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• 2024

This study evaluated a method for prompt and periodic monitoring of moisture content in the waste layer of a landfill using a Time Domain Reflectometry (TDR) sensor, aimed at ensuring the stable operation of the bioreactor facility at the 2nd landfill of the SUDOKWON Landfill Site. It was found that the TDR sensor is sensitive to variables such as temperature changes and ion content in both the waste layer and leachate, indicating that a correction equation is necessary. A correction equation derived through regression analysis demonstrated a high correlation (correlation coefficient = 0.9647), and field verification experiments confirmed its reliability with an average deviation of only 1.5%. This verifies that the TDR sensor is effective for measuring and monitoring moisture content in landfills. It is also anticipated to be useful for various applications, including monitoring leachate levels, detecting leachate leakage, and assessing rainwater infiltration.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.89-99
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• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Journal of the Korean Geotechnical Society
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• v.39 no.5
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• pp.13-26
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• 2023

This study examined the soil-water characteristic curve (SWCC), considering the volume change, using wetting curves on the field monitoring data of a wireless sensor network. Special attention was given to evaluating the landslide vulnerability by deriving a matric suction suitable for the actual site during the wetting process. Laboratory drying SWCC and shrinkage laboratory tests were used to perform the combined analysis of landslide and debris flow. The results showed that the safety factor of the wetting curve, considering the volume change of soil, was lower than that of the drying curve. As a result of numerical analyses of the debris flow simulation, more debris flow occurred in the wetting curve than in the drying curve. It was also found that the landslide analysis with the drying curve tends to overestimate the actual safety factor with the in situ wetting curve. Finally, it is confirmed that calculating the matric suction through SWCC considering the volume change is more appropriate and reasonable for the field landslide analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.130-137
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• 2023

Smart farms, which have been receiving attention as a solution to recent rural problems, refer to technologies that optimize the growing environment of crops and increase the productivity and quality of crops through efficient management. If the relationships between environmental data in smart farms are analyzed, additional productivity enhancement and crop management will be possible. In this paper, we propose a method for acquiring and analyzing nine environmental data, including temperature, humidity, CO2, soil temperature, soil moisture, insolation, soil EC, EC, and pH. Data acquisition is done through RS-485 communication between the main board and the sensor board and stored in the database after acquisition. The stored data is downloaded in Excel sheet format and analyzed through histograms, data charts, and correlation heatmaps. First, we analyze the distribution of total, day, and night data through histogram analysis, and identifiy the average, median, minimum, and maximum values by month through data chart analysis separating day and night to see how the data changes by month. Finally, we analyze the correlation of the data through a correlation heatmap analysis separating day and night. The results show a very strong positive correlation between temperature and soil temperature and soil EC and EC during the day, and a very strong positive correlation between temperature and soil temperature and soil EC and EC at night, and a strong negative correlation between temperature and soil EC.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.165-177
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• 2015

Purpose: Accurate monitoring of soil strength is a key technology applicable to various precision agricultural practices. Soil strength has been traditionally measured using a cone penetrometer, which is time-consuming and expensive, making it difficult to obtain the spatial data required for precision agriculture. To improve the current, inefficient method of measuring soil strength, our objective was to develop and evaluate an in-situ system that could measure horizontal soil strength in real-time, while moving across a soil bin. Methods: Multiple cone-shape penetrometers were horizontally assembled at the front of a vertical plow blade at intervals of 5 cm. Each penetrometer was directly connected to a load cell, which measured loads of 0-2.54 kN. In order to process the digital signals from every individual transducer concurrently, a microcontroller was embedded into the measurement system. Wireless data communication was used between a data storage device and this real-time horizontal soil strength (RHSS) measurement system travelling at 0.5 m/s through an indoor experimental soil bin. The horizontal soil strength index (HSSI) measured by the developed system was compared with the cone index (CI) measured by a traditional cone penetrometer. Results: The coefficient of determination between the CI and the HSSI at depths of 5 cm and 10 cm ($r^2=0.67$ and 0.88, respectively) were relatively less than those measured below 20 cm ($r^2{\geq}0.93$). Additionally, the measured HSSIs were typically greater than the CIs for a given numbers of compactor operations. For an all-depth regression, the coefficient of determination was 0.94, with a RMSE of 0.23. Conclusions: A HSSI measurement system was evaluated in comparison with the conventional soil strength measurement system, CI. Further study is needed, in the form of field tests, on this real-time measurement and control system, which would be applied to precision agriculture.