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    Evaluation of the Clemson instrumented subsoiler shank in coastal plain soils
    (Elsevier Sci Ltd, 2014) Khalilian, A.; Han, Y. J.; Marshall, M. W.; Gorucu, S.; Abbaspour-Gilandeh, Y.; Kirk, K. R.
    Most sandy soils in coastal plains of the southeastern USA have a compacted zone or hardpan which limits root penetration below the plowing depth, reducing yields, and predisposing plants to drought stress. The hardpan layer exhibits a great amount of variability in depth and thickness in this region. Real-time, sensor-based, site-specific tillage could achieve significant savings in energy requirements for subsoiling and increase crop yields. Replicated tests were conducted to evaluate the performance of the Clemson instrumented subsoiler shank under actual field conditions. The instrumented subsoiler shank was calibrated against cone penetrometer readings on three coastal plain soil types. A strong positive correlation between soil strength values measured with the penetrometer and the instrumented subsoiler shank was observed (R-2 = 0.89-0.97). On average, the shank index values (measured horizontally) were about 50% less than the corresponding cone index values (measured vertically). The effect of soil moisture content on shank-penetrometer correlation was not significant (alpha = 0.05). It is possible to determine the depth and thickness of the hardpan layers with the instrumented subsoiler shank either for real time control of subsoiling location and depth or for generating site-specific tillage maps. (C) 2014 Elsevier B.V. All rights reserved.
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    REFLECTANCE-BASED SENSOR TO PREDICT VISUAL QUALITY RATINGS OF TURFGRASS PLOTS
    (Amer Soc Agricultural & Biological Engineers, 2008) Keskin, M.; Han, Y. J.; Dodd, R. B.; Khalilian, A.
    Turfgrass quality is visually evaluated by human assessors based on a settle of 1 to 9. This evaluation practice is subjective and does not provide accurate and reproducible measure of the turf quality. The aim of this research was to design a portable optical sensor to predict the quality ratings of turfgrass research plots from spectral reflectance. Reflectance data were collected using a dual spectroradiometer covering a spectrum of 350-1050 nm front bermudagrass and bluegrass research plots. Two different regression methods, Multiple Linear Regression (MLR) and Partial Least Squares Regression (PLSR), were used and compared. Two wavelength bands centered at 680 nm (Red) and 780 nm (NIR) were identified since these bands carry useful information in the prediction of turfgrass visual quality The average Standard Error of Cross Validation (SECV) was found to be about 0.76 and 0.88 by using the model with Red and NIR bands for bermudagrass and bluegrass data sets, respectively. A simple prototype sensor using the two identified bands was fabricated and tested. The prototype sensor predicted the visual quality ratings as well as the spectroradiometer with a SECV of about 0.57 using two bands.