Young J. Han

ASSESSING NITROGEN CONTENT OF GOLF COURSE TURFGRASS CLIPPINGS USING SPECTRAL REFLECTANCE

Feasibility of a practical indoor reflectance-based sensor was studied to assess nitrogen content of turfgrass clippings from spectral reflectance data. Turfgrass clipping samples were obtained from commercial golf course putting greens, and their reflectance were measured using a dual-type spectroradiometer under artificial illumination 3 h and 51 h after mowing. The reflectance values in green band (520 to 580 nm) and the NIR band (770 to 1050 nm) increased as the nitrogen content increased. Four wavelength bands at 550, 680, 770, and 810 nm were selected to develop and compare several regression models with varying number of variables. All models performed well (R2 > 0.82) and predicted the nitrogen content with reasonable standard error of prediction (SEP) values (SEP < 0.62%) for the data taken 3 h after mowing. However, the data taken 51 h after mowing on the same samples did not yield good results (R2 1.04%). A discriminant analysis showed that the regression model with two wavelength variables performed as well as the models with a higher number of variables. A simple reflectance sensor using even only one photodiode and bandpass filter can be developed to predict or classify the nitrogen content of turfgrass clippings when the reflectance data are taken within several hours of mowing.

VARIABLE DEPTH TILLAGE BASED ON GEO-REFERENCED SOIL COMPACTION DATA IN COASTAL PLAIN REGION OF SOUTH CAROLINA

This study deals with determining the optimum tillage depth from geo-referenced soil compaction and soil electrical conductivity data. Soil compaction data was analyzed and mapped. Based on the measurements, 75% of the test field required shallower tillage depth than recommended tillage depth for coastal plain soils. Variable depth tillage, no-tillage and conventional tillage systems were compared and the relationships between tillage depth, soil electrical conductivity, crop responses, and yield were investigated in cotton production. The results showed that the main factor in yield was soil texture. Also, the energy savings of 15.82 kW.h (42.8%) and fuel saving of 4.58 L (28.4%) could be achieved by adoption of variable-depth tillage as compared to uniform-depth tillage.

AN ALGORITHM TO DETERMINE THE OPTIMUM TILLAGE DEPTH FROM SOIL PENETROMETER DATA IN COASTAL PLAIN SOILS

Soil compaction is a significant problem in the southeastern Coastal Plain soils. Cone penetrometers are used widely for soil strength measurement and tillage decisions. However, there is no standard or comprehensive method for determining the optimum tillage depth from soil penetrometer data in Coastal Plain soils. Our objective was to develop an algorithm for determining the optimum tillage depth from soil cone penetrometer measurements to effectively remove the hardpan. Intensive geo-referenced soil cone penetrometer measurements were obtained and each cone index profile was graphically examined. The results showed six main patterns for penetrometer profiles in a Dothan loamy sand soil. An algorithm and computer program was developed to determine the optimum tillage depth from the penetrometer data taken in a two-year study (2000 and 2001). The results clearly indicated that the thickness and the location of the hardpan can be determined from the soil cone penetrometer data. A great amount of variation was observed in the depth and the thickness of the hardpan (4 to 25 cm) as well as in the optimum tillage depth (25 to 45 cm) in both years.

Identification and Measurement of Convolutions inCotton Fiber Using Image Analysis

An image analysis procedure was developed to quantify morphological characteristics of convolutions in individual cotton fibers without pre-tensioning or orientation requirements. The image of each fiber was captured by a PC-based color imaging system using a conventional microscope. Ends of individual cotton fibers were glued on a microscope slide without any tension or straightening. A modified watershed technique was implemented to identify individual convolution segments, which were defined as sections of the fiber bordered by two neighboring convolutions. Length, area and perimeter of each convolution segment were measured directly from the image. Average width, shape factor and number of convolution segments in mm were calculated from the measured parameters. Performance of the image analysis algorithm was compared with visual inspection for number and position of convolution segments in three different varieties of cotton. Image analysis results agreed with visual inspection in 89.6% of the tested images.

Reflectance-Based Sensor to Predict Visual Quality Ratings of Turfgrass Plots

Turfgrass quality is visually evaluated by human assessors based on a scale 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 from 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.

Development of a Yield Monitor for Peanut Research Plots

A yield monitoring system for a peanut combine was developed to record harvest data from research test plots. The system collects a batch of peanuts from each research plot into a weighing bin, weighs the batch, dumps the batch into a duct, and then pneumatically conveys the batch to the primary basket. The weighing bin is suspended from load cells connected to a monitor. For the conveyance cycle, an electrical control circuit operates a 12 VDC gear motor coupled to a hatch in the bottom of the weighing bin. Peanuts are conveyed from the air duct to the primary basket using a centrifugal blower driven by a hydraulic motor. A small hopper was included to collect samples from each batch for quality analyses. Conventional research plot harvesting required three individuals: a tractor operator, someone to bag samples from the harvester and haul sacks, and someone to weigh the peanut sacks. The developed system reduced the harvest operation to only one individual when quality samples are not required and to two individuals when required. Preliminary field trials indicated that the system was successful in collecting, accurately weighing, and conveying the peanuts but that additional work is necessary relative to sampling and geo-referencing data acquisition.

Application of Laser Beams to Apple Firmness Measurement

Abstract A laser beam was used as a light source to evaluate finnness of apples nondestructively. Two different varieties of apples were illuminated with laser beams with three different wavelengths at three output power levels. Five features of interest were derived from laser images to estimate the fruit firmness using multiple regression analysis. The actual firmness was measured by an Instron Universal Tester. A multivariate discrimination analysis technique was used to classify apples into various finnness stages.

Developing an Algorithm to Determine the Tillage Depth from Soil Penetrometer Data in Coastal Plain Soils

This study deals with developing an algorithm to determine the optimum tillage depth from soil cone penetrometer data in coastal plain soils. Intensive geo-referenced soil cone penetrometer measurements were obtained and each cone index profile was graphically examined. The results showed 21 different patterns or conditions for the soil cone penetrometer profiles in a Dothan loamy sand soil. An algorithm was developed to assess and find the optimum tillage depth. The limiting cone index value, which prevents the root development, was taken as 2.07 MPa (300 psi). After defining the algorithm, a computer program was written to automate the application of the algorithm for large data sets. The computer program was used to find the optimum tillage depth for the data taken in the two-year study (2000 and 2001). The results of the experimental study clearly indicate that the thickness and the location of the hardpan can be determined from the soil cone penetrometer data. A great amount of variation was observed in the location and the thickness of the hardpan as well as in the optimum tillage depth in both years.

Development of a Portable Electronic Nose for Detection of Cotton Damaged by Nezara viridula (Hemiptera: Pentatomidae)

Stink bugs are significant pests of cotton in the southeastern USA, causing millions of dollars in control costs and crop losses each year. New methods to detect stink bug damage must be investigated in order to reduce these costs and optimize pesticide applications. One such method would be to detect the volatile organic compounds (VOCs) emitted from cotton plants damaged by stink bugs. A portable device was developed to draw VOCs from the head space of a cotton boll over carbon black-polymer composite sensors. From the response of these sensors, this device would indicate if the boll was fed upon by a stink bug or not. The device was 100% accurate in distinguishing bolls damaged by stink bugs from undamaged controls when tested under training conditions. However, the device was only 57.1% accurate in distinguishing damaged from undamaged bolls when tested 24 h after it was trained. These results indicated that this device was capable of classifying cotton as damaged or undamaged by differentiating VOCs released from undamaged or damaged bolls, but improvements in design are required to address sensitivity to fluctuations in environmental conditions.

Instrumentation for Variable-Rate Lateral Irrigation System

Crops in the Southern United States are generally produced in fields which are known to have a high degree of variability in soil type, topography, water holding capacity and other major factors which affect crop production. Therefore, conventional, uniform-rate overhead irrigation systems tend to over-apply or under-apply water to the crop. A variable-rate lateral irrigation system was developed for site-specific application of water to match crop needs. This system is able to monitor and apply water based on the actual soil moisture content, pan evaporation data, or the U.S. Climate Reference Network (CRN) data. Information from the moisture sensors, evaporation pan and CRN is acquired using wireless technology. A GPS receiver is used to determine the position of the lateral irrigation system in the field. A variable speed control system allows the irrigation system to move quickly over wet spots and slow down over dry spots. The lateral irrigation system is controlled by the nozzle-pulsing technique for variable-rate water application. The nozzle pulsing technique to adjust irrigation rate worked very well. The average water application rate error was less than 2%. There was a strong correlation between soil electrical conductivity (EC) and soil water holding capacity. Therefore, the EC measurements could be used for irrigation scheduling decisions.