Doctor of Philosophy / Department of Biological & Agricultural Engineering / Naiqian Zhang / A two-tier wireless data communication system was developed to remotely monitor sediment concentration in streams in real time. The system used wireless motes and other devices to form a wireless sensor network to acquire data from multiple sensors. The system also used a Stargate, a single-board computer, as a gateway to manage and control data flow and wireless data transfer. The sensor signals were transmitted from an AirCard on the Stargate to an Internet server through the General Packet Radio Service (GPRS) provided by a commercial GSM cellular carrier. Various types of antennas were used to boost the signal level in a radio-hostile environment. Both short- and long-distance wireless data communications were achieved. Power supplies for the motes, Stargate, and AirCard were improved for reliable and robust field applications. The application software was developed using Java, C, nesC, LabView, and SQL to ensure seamless data transfer and enable both on-site and remote monitoring. Remote field tests were carried out at different locations with different GPRS signal strengths and a variety of landscapes. A three-tier wireless sensor network was then developed and deployed at three military installations around the country – Fort Riley in Kansas, Fort Benning in Georgia, and Aberdeen Proving Ground in Maryland - to remotely monitor sediment concentration and movement in real time. Sensor nodes, gateway stations, repeater stations, and central stations were strategically deployed to insure reliable signal transmissions. Radio signal strength was tested to analyze effects of distance, vegetation, and topographical barriers. Omni- and Yagi-directional antennas with different gains were tested to achieve robust, long-range communication in a wireless-hostile environment. Sampling times of sensor nodes within a local sensor network were synchronized at the gateway station. Error detection algorithms were developed to detect errors caused by interference and other impairments of the transmission path. GSM and CDMA cellular modems were used at different locations based on cellular coverage. Data were analyzed to verify the effectiveness and reliability of the three-tier WSN.

Master of Science / Department of Biological and Agricultural Engineering / Naiqian Zhang / In order to meet food, fiber, and bio-fuel needs of a growing world population, crop-breeding methods must be improved and new technologies must be developed. One area under focus is the decoding of the genetic basis of complex traits, such as yield and drought stress tolerance, and predicting these traits from genetic composition of lines or cultivars. In the last three decades, significant advances in genotyping methods have resulted in a wealth of genomic information; however, little improvement has occurred for methods of collecting corresponding plant trait data, especially for agronomic crops. This study developed a mobile, field-based, high-throughput sensor platform for rapid and repeated measurement of plant characteristics. The platform consisted of three sets of sensors mounted on a high-clearance vehicle. Each set of sensors contained two infrared thermometers (IRT), one ultrasonic sensor, one Crop Circle, and one GreenSeeker. Each sensor set measured canopy temperature, crop height, and spectral reflectance. In addition to the sensors, the platform was equipped with an RTK-GPS system that provided precise, accurate position data for georeferencing sensor measurements. Software for collecting, georeferencing, and logging sensor data was developed using National Instruments LabVIEW and deployed on a laptop computer. Two verification tests were conducted to evaluate the phenotyping system. In the first test, data timestamps were analyzed to determine if the system could collect data at the required rate of 10 Hz and 5 Hz for sensor data and position data, respectively. The determination was made that, on average, IRT, ultrasonic, and Crop Circle data are received in intervals of 100 ms (SD = 10 ms), GreenSeeker data are received in intervals of 122 ms(SD=10 ms), and position data are received in intervals of 200 ms (SD = 32 ms). The second test determined that a statistically significant relationship exists between sensor readings and ambient light intensity and ambient temperatures. Whether the relationship is significant from a practical stand point should be determined based on specific application of the sensors.

Surface soil samples from a climosequence were studied with the purpose of relating color, reflectance variations and texture to contents of organic carbon and free iron oxides. Information on the physicochemical properties of the soils were obtained with a fine resolution spectroradiometer, a chromameter and by laboratory analyses. The effect of soil organic carbon and free iron oxides is shown by the varying shape of the soil spectral curves. Both the chromameter and the spectroradiometer detected the varying amounts of organic carbon and free iron oxides in soil. Silt had a positive, highly significant relationship with organic carbon. Clay and silt had a positive highly significant relationship with free iron oxides.

Gravity bubbler irrigation is a new mode of irrigation activated by the existing pressure in conventional irrigation supply channels. In gravity flow systems on steep slopes, pressure increases in the downstream sections of the pipe and must be dissipated for uniform application. A design procedure for gravity bubbler irrigation systems on inclined steep slopes or converted to bench terrace systems is described in detail. The design is based on the use of orifices as energy dissipating devices. Laboratory tests were conducted to determine graphical relationships and coefficients for estimating the head loss for an orifice made from PVC. The head loss coefficient is a function of the orifice to pipe diameter ratio and can be expressed by an equation of the form Ko = abetab where a and b are constants determined from test data and beta is the ratio of diameters. A prototype gravity bubbler irrigation system was designed and installed.

Nutrient mitigation capacity of low-grade weirs in agricultural drainage ditches

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3629. Advisers: Xinlei Wang; Yuanhui Zhang. Includes bibliographical references (leaves 86-98) Available on microfilm from Pro Quest Information and Learning.

Remote sensing is a tool that is increasingly used in agriculture for crop management purposes. A ground-based remote sensing data acquisition system was designed, constructed, and implemented to collect high spatial and temporal resolution data in irrigated agriculture. The system was composed of a rail that mounts on a linear move irrigation machine, and a small cart that runs back and forth on the rail. The cart was equipped with a sensors package that measured reflectance in four discrete wavelengths (550 nm, 660 nm, 720 nm, and 810 nm, all 10 nm bandwidth) and an infrared thermometer. A global positioning system and triggers on the rail indicated cart position. The data was postprocessed in order to generate vegetation maps, N and water status maps and other indices relevant for site-specific crop management. A geographic information system (GIS) was used to generate images of the field on any desired day. The system was named AgIIS (A̲gricultural I̲rrigation I̲maging S̲ystem). This ground based remote sensing acquisition system was developed at the Agricultural and Biosystems Engineering Department at the University of Arizona in conjunction with the U.S. Water Conservation Laboratory in Phoenix, as part of a cooperative study primarily funded by the Idaho National Environmental and Engineering Laboratory. A second phase of the study utilized data acquired with AgIIS during the 1999 cotton growing season to model petiole nitrate (PNO₃⁻) and total leaf N. A latin square experimental design with optimal and low water and optimal and low N was used to evaluate N status under water and no water stress conditions. Multivariable models were generated with neural networks (NN) and multilinear regression (MLR). Single variable models were generated from chlorophyll meter readings (SPAD) and from the Canopy Chlorophyll Content Index (CCCI). All models were evaluated against observed PNO₃⁻ and total leaf N levels. The NN models showed the highest correlation with PNO₃⁻ and total leaf N. AgIIS was a reliable and efficient data acquisition system for research and also showed potential for use in commercial farming systems.

A data acquisition system to collect soil moisture readings at 60 field locations was developed. The system predicted a resistance value from a measured counts per time. An error was associated with the measured counts and time, however, this error was minimized by increasing the time for resistance measurement. The effect of temperature was minimized by an automatic calibration of the system before collecting readings. The Watermark electrical resistance moisture sensor was used to sense water content. The system, including eight sensors, was tested in the field. The data collected was difficult to explain. An evaluation of the Watermark sensors indicated a large variation from sensor to sensor, and also indicated a marked influence of soil texture on sensor resistance.

Subsurface drip irrigation was compared to sprinkler irrigation on bermudagrass turf during three consecutive years using tertiary treated wastewater. Irrigation amount required by each treatment, visual appearance of the grass, shoot biomass production, and soil salinity were measured, and potential management problems were identified. The amount of irrigation water applied via subsurface irrigation was similar or higher than that applied via sprinkler irrigation for a turf of similar quality. Shoot biomass production did not differ between both irrigation methods when similar amounts of water were applied. Soil salinity, measured as electrical conductivity, was monitored at the beginning and end of each season. The changes in electrical conductivity at the end of every irrigation season did not negatively affect the appearance of the turf in any of the years. Emitter clogging by root intrusion was identified as a potential problem in the subsurface drip irrigation system. A series of greenhouse experiments were conducted to evaluate the effect of different herbicides and acids at several concentrations on root intrusion into subsurface drip emitters. The first greenhouse experiment was a study intended to identify chemical concentrations that could inhibit bermudagrass root growth in soil without negatively affecting the visual appearance of the grass. As a result, two herbicides, trifluralin and thiazopyr, and one acid, phosphoric acid, were selected for a second greenhouse experiment. The second greenhouse experiment focused on the effects of the two herbicides and the acid on root intrusion into subsurface drip emitters. Only the emitters treated with thiazopyr at the highest dose were completely clean, root-free emitters.

This research has developed a method for estimating hydraulic roughness coefficients for overland flow models in a dynamic approach, to more effectively simulate runoff on natural, agricultural and urban slopes. The hydraulic roughness coefficients are then generated with a series of neural networks. First, a laboratory experiment was designed to explore the effects of soil microtopography, slope and Reynolds number on the magnitude of Darcy-Weisbach, Manning and Chezy roughness coefficients. It was found that three parameters were necessary to describe the soil surface microtopography. Neural networks developed in a preliminary phase were able to reproduce the roughness coefficients obtained in the laboratory experiment by using five predictor variables: bed slope, Reynolds number, and the three parameters used to describe the microtopography. However, these networks failed to generate roughness coefficients for different input variables (generalization). Second, more complex algorithms were developed as combinations of neural networks in parallel. The algorithm output, the sought hydraulic roughness estimate, was estimated with the arithmetic average of the individual network outputs. Results presented in this study demonstrate that combining multiple neural networks reduced the prediction error and improved on the generalization ability of the neural networks. It was also observed that the estimate accuracy was influenced by the characteristics of the dataset, and especially by the relationship between the roughness coefficient and Reynolds numbers. Finally, a field experiment was performed to explore the applicability of the algorithms. A numerical model based on the 1-D diffusion approximation to the Saint Venant equations was constructed, and two surface irrigations were performed to collect data to test the model estimates. The model was used under two scenarios: (1) with constant hydraulic roughness coefficients, and (2) using variable hydraulic roughness predicted with the algorithm. Discharge at the end of the plot and irrigation front advance estimated using both models matched the observations well. However, when using a variable hydraulic roughness, the front was initially delayed until there was a sufficient surface storage to push it forward. The methodology described in this research should be useful for 2-D overland flow models applied to natural slopes with unsteady rainfall.