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RISS 인기검색어
- 검색키워드
- 저자 : Kenneth Sudduth (검색결과 28 건)
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Cho, Yongjin,Sudduth, Kenneth A.,Chung, Sun-Ok Elsevier 2016 Biosystems engineering Vol.152 No.-
<P>Proximal soil sensing is an attractive approach for quantifying soil properties, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (EC<SUB>a</SUB>) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and EC<SUB>a</SUB> data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, EC<SUB>a</SUB> measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. An EC<SUB>a</SUB> model inversion approach was used to generate layer EC values corresponding to the depth increments of the other variables. Fits of models using EC to estimate WC were variable (R<SUP>2</SUP> = 0.31–0.79). Best fitting BD estimation models (R<SUP>2</SUP> = 0.11–0.55) generally included EC, but soil strength was included in fewer than half of the models. BD model fits were improved considerably by adding lab-measured WC to the model (R<SUP>2</SUP> = 0.30–0.86), suggesting the need for a WC sensor. Soil clay texture fraction models based on EC and WC fit well (R<SUP>2</SUP> = 0.80–0.93). This study showed the potential of combining data from multiple mobile proximal sensors to estimate important soil physical properties.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Soil physical properties were estimated from soil sensor measurements. </LI> <LI> Properties examined were water content (WC), bulk density, and clay fraction. </LI> <LI> Best WC estimates used both soil strength and apparent electrical conductivity (EC<SUB>a</SUB>). </LI> <LI> Best bulk density estimates included lab-measured water content in the model. </LI> <LI> Clay texture fraction was well-estimated at most measurement depths by EC<SUB>a</SUB>. </LI> </UL> </P>
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On-the-go Soil Strength Profile Sensor to Quantify Spatial and Vertical Variations in Soil Strength
Chung, Sun-Ok,Sudduth, Kenneth A. Korean Society for Agricultural Machinery 2005 Agricultural and Biosystems Engineering Vol.6 No.2
Because soil compaction is a concern in crop production and environmental pollution, quantification and management of spatial and vertical variability in soil compaction for soil strength) would be a useful aspect of site -specific field management. In this paper, a soil strength profile sensor (SSPS) that could take measurements continuously while traveling across the field was developed and the performance was evaluated through laboratory and field tests. The SSPS obtained data simultaneously at 5 evenly spaced depths up to 50 em using an array of load cells, each of which was interfaced with a soil-cutting tip. Means of soil strength measurements collected in adjacent, parallel transects were not significantly different, confirming the repeatability of soil strength sensing with the SSPS. Maps created with sensor data showed spatial and vertical variability in soil strength. Depth to the restrictive layer was different for different field locations, and only 5 to 16% of the tested field areas were highly compacted.
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Relating Hyperspectral Image Bands and Vegetation Indices to Corn and Soybean Yield
Jang Gab-Sue,Sudduth Kenneth A.,Hong Suk-Young,Kitchen Newell R.,Palm Harlan L. The Korean Society of Remote Sensing 2006 大韓遠隔探査學會誌 Vol.22 No.3
Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.
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Sensing Nitrate and Potassium Ions in Soil Extracts Using Ion-Selective Electrodes
Kim, H.J.,Sudduth Kenneth A.,Hummel John W. Korean Society for Agricultural Machinery 2006 바이오시스템공학 Vol.31 No.6
Automated sensing of soil macronutrients would allow more efficient mapping of soil nutrient spatial variability for variable-rate nutrient management. The capabilities of ion-selective electrodes for sensing macronutrients in soil extracts can be affected by the presence of other ions in the soil itself as well as by high concentrations of ions in soil extractants. Adoption of automated, on-the-go sensing of soil nutrients would be enhanced if a single extracting solution could be used for the concurrent extraction of multiple soil macronutrients. This paper reports on the ability of the Kelowna extractant to extract macronutrients (N, P, and K) from US Corn Belt soils and whether previously developed PVC-based nitrate and potassium ion-selective electrodes could determine the nitrate and potassium concentrations in soil extracts obtained using the Kelowna extractant. The extraction efficiencies of nitrate-N and phosphorus obtained with the Kelowna solution for seven US Corn Belt soils were comparable to those obtained with IM KCI and Mehlich III solutions when measured with automated ion and ICP analyzers, respectively. However, the potassium levels extracted with the Kelowna extractant were, on average, 42% less than those obtained with the Mehlich III solution. Nevertheless, it was expected that Kelowna could extract proportional amounts of potassium ion due to a strong linear relationship ($r^2$ = 0.96). Use of the PVC-based nitrate and potassium ion-selective electrodes proved to be feasible in measuring nitrate-N and potassium ions in Kelowna - soil extracts with almost 1 : 1 relationships and high coefficients of determination ($r^2$ > 0.9) between the levels of nitrate-N and potassium obtained with the ion-selective electrodes and standard analytical instruments.
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Development of User Terminal Software for Korean Grain Yield Monitoring Systems
Lee Kyu-Ho,정선옥,Sudduth Kenneth A. 한국농업기계학회 2022 바이오시스템공학 Vol.47 No.3
Purpose In yield monitoring systems, user terminal software plays a role in collecting harvest data and creating yield maps. The software developed in this study was made specifically for Korean grain yield monitoring systems. Methods The main functions of the software were based on commercial yield monitoring software, but modified for Korean systems. In order to demonstrate accuracy and performance of the yield monitoring system, we conducted indoor and field tests. Through the indoor test, we demonstrated functions of the software such as automatic position filter and delay time filter with yield data collected in the USA and the Republic of Korea. To prove the accuracy of the software in yield estimation, six field tests were conducted, three at 1.5 m/s, and the other three at 1.7 m/s. Software results were compared to manual weight and moisture content measurements collected from the grain tank. Results In the indoor test, the automatic position filter removed unideal position data (1.92%) and average accuracy of the sensors after applying the calibration function was 91%. After applying the delay time function, yield maps became more coherent in spatial yield patterns. Through the field test, the error rate of yield and moisture content were 4.24 and 2.51%, respectively. Conclusions The user terminal software developed in this study has potential for use in Korean grain yield monitoring systems, but should be improved to better consider the Korean harvesting environment.
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( Gi Young Kim ),( Kenneth A. Sudduth ),( Sheila A. Grant ),( Newell R. Kitchen ) 한국농업기계학회 2012 바이오시스템공학 Vol.37 No.3
Purpose: This study was performed to develop a simple, disposable thin-film optical nitrate sensor. Methods: The sensor was fabricated by applying a nitrate-selective polymer membrane on the surface of a thin polyester film. The membrane was composed of polyvinylchloride (PVC), plasticizer, fluorescent dye, and nitrate-selective ionophore. Fluorescence intensity of the sensor increased on contact with a nitrate solution. The fluorescence response of the optical nitrate sensor was measured with a commercial fluorospectrometer. Results: The optical sensor exhibited linear response over four concentration decades. Conclusions: Nitrate ion concentrations in plant nutrient solutions can be determined by direct optical measurements without any conditioning before measurements.
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Comparison of Remote Sensing and Crop Growth Models for Estimating Within-Field LAI Variability
Hong, Suk-Young,Sudduth, Kenneth-A.,Kitchen, Newell-R.,Fraisse, Clyde-W.,Palm, Harlan-L.,Wiebold, William-J. The Korean Society of Remote Sensing 2004 大韓遠隔探査學會誌 Vol.20 No.3
The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.
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