PRECISE EVALUATION OF GNSS POSITION AND LATENCY ERRORS IN DYNAMIC AGRICULTURAL APPLICATIONS

A method for precisely synchronizing an external serial data stream to the pulse-per-second (PPS) output signal from a global navigation satellite-based system (GNSS) receiver was investigated. A signal timing device was designed that used a digital signal processor (DSP) with serial inputs and input captures to generate time stamps for asynchronous serial data based on an 58593.75 Hz internal timer. All temporal measurements were made directly in hardware to eliminate software latency. The resolution of the system was 17.1 µs, which translated to less than one millimeter of horizontal position error at travel speeds typical of most agricultural operations. The dynamic error of a TTS was determined using a rotary test fixture. Tests were performed at angular velocities ranging from 0 to 3.72 rad/s and a radius of 0.635 m. Average latency from the TTS was shown to be consistently near 0.252 s for all angular velocities and less variable when using a reflector based machine target versus a prism target. Sight distance from the target to the TTS was shown to have very little effect on accuracy between 4 and 30 m. The TTS was determined to be a limited as a position reference for dynamic GNSS and vehicle auto-guidance testing based on angular velocity. The dynamic error of a GNSS receiver was determined using the rotary test fixture and modeled as discrete probability density functions for varying angular velocities and filter levels. GNSS position and fixture data were recorded for angular velocities of 0.824, 1.423, 2.018, 2.618, and 3.222 rad/s at a 1 m radius. Filter levels were adjusted to four available settings including; no filter, normal filter, high filter, and max filter. Each data set contained 4 hours of continuous operation and was replicated three times. Results showed that higher angular velocities increased the variability of the distribution of error while not having a significant effect on average error. The distribution of error tended to change from normal distributions at lower angular velocities to uniform distributions at higher angular velocities. Internal filtering was shown to consistently increase dynamic error for all angular velocities

A Low-Cost Method for Collecting Hyperspectral Measurements from a Small Unmanned Aircraft System

Small unmanned aircraft systems (UAS) are a relatively new tool for collecting remote sensing data at dense spatial and temporal resolutions. This study aimed to develop a spectral measurement platform for deployment on a UAS for quantifying and delineating moisture zones within an agricultural landscape. A series of portable spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using a Raspberry Pi embedded computer that was programmed to interface with the UAS autopilot for autonomous data acquisition. A second set of identical spectrometers were fitted with calibrated irradiance lenses to capture ambient light during data acquisition. Data were collected during the 2017 Great American Eclipse while observing a reflectance target to determine the ability to compensate for ambient light conditions. A calibration routine was developed that scaled raw reflectance data by sensor integration time and ambient light energy. The resulting calibrated reflectance exhibited a consistent spectral profile and average intensity across a wide range of ambient light conditions. Results indicated the potential for mitigating the effect of ambient light when passively measuring reflectance on a portable spectral measurement system. Future work will use multiple reflectance targets to test the ability to classify targets based on spectral signatures under a wide range of ambient light conditions

Monitoring Yogurt Culture Fermentation and Predicting Fermentation Endpoint with Fluorescence Spectroscopy

Determination of the endpoint of yogurt culture fermentation is a process parameter that could benefit from automation. The feasibility of using a fluorescence sensor technology based on 280 nm excitation and 350 nm emission to predict the endpoint of yogurt culture fermentation was investigated and compared with the endpoint prediction from a near-infrared (880 nm) light backscatter sensor. Yogurt cultures with three levels of milk solids (8%, 10%, and 12%) and three temperatures (40°C, 43°C, and 46°C) were tested with three replications in a 3 x 3 factorial design (n = 27). Prediction models were developed for each optical measurement using the independent variables and time parameters calculated from the data. It was found that the fluorescence sensor technology was able to predict the endpoint of yogurt culture fermentation with a standard error of 16.0 min and an R2 value of 0.999. The near-infrared sensor technology was able to predict the endpoint with a standard error of 10.4 min and an R2 value of 0.997

Scalable Control Architecture for Variable-Rate Turn Compensation

The objective of this study was to determine if a CAN bus could be used to implement variable-rate turn compensation in a manner that is scalable by encoding application rates for an entire implement into a single data message. A variable-rate turn compensation test fixture was developed that used a CAN bus to communicate application rates to 16 individual nodes using a 2-byte data message (80-bit extended identifier CAN messages). The system assumed that the physical structure of an implement was linear and that the control nodes were equally spaced. Application rates for the outer-most nodes were broadcasted and the remaining nodes calculated their application rate using a linear interpolation method. Node locations were determined using a 4-bit binary thumbwheel switch located at each control node, allowing all nodes to run an identical program. Servo-controlled gauges were used to visualize node application rate across the test fixture. A joystick interface was developed to simulate vehicle movements and desired application rates. The system transmitted Bluetooth serial messages at a rate of 20 Hz, which were received by the test fixture and converted to CAN messages before being broadcasted to the control nodes. Two USB to CAN interfaces were connected to the CAN bus to insert additional traffic and measure bandwidth utilization. Due to the minimal amount of bandwidth required (\u3c1%) to transmit variable-rate control messages, the system functioned properly when the CAN bus was heavily loaded with traffic up to 99% of the available bandwidth of 250 kbps. The variable-rate turn compensation test fixture demonstrated that a CAN bus is a suitable protocol for communicating variable-rate data. The scalable encoding technique developed in this study resulted in a single message required to update all nodes, regardless of the number of nodes in the system. The system has broad applicability in future planting, fertilizing, and chemical application systems where deposition points are evenly spaced along an implement

Development and Preliminary Evaluation of an Integrated Individual Nozzle Direct Injection and Carrier Flow Rate Control System for Pesticide Applications

Direct injection systems for agricultural spray applications continue to present challenges in terms of commercialization and adoption by end users. Such systems have typically suffered from lag time and mixing uniformity issues, which have outweighed the potential benefits of keeping chemical and carrier separate or reducing improper tank-mixed concentration by eliminating operator measurements. The proposed system sought to combine high-pressure direct nozzle injection with an automated variable-flow nozzle to improve chemical mixing and response times. The specific objectives were to: (1) integrate a high-pressure direct nozzle injection system with variable-flow carrier control into a prototype for testing, (2) assess the chemical metering accuracy and proper mixing at different combinations of injection valve frequency and duty cycle along with chemical pressure, and (3) assess the ability of the control system to ensure proper chemical dilutions and concentrations in the nozzle effluent resulting from step changes in target application rates. Laboratory experiments were conducted using the combined system. Results of these experiments showed that the open-loop control of the injectors could provide a means of accurately metering the chemical concentrate into the carrier stream. Chemical injection rates could be achieved with an average error of 5.4% compared to the target rates. Injection at higher duty cycles resulted in less error in the chemical concentration predictions. Discrete Fourier transform analysis showed that the injection frequency was noticeable in the nozzle effluent when the injector was operated at 3.04 MPa and 5 Hz (particularly at lower duty cycles). Increasing the injection pressure and operating frequency to 5.87 MPa and 7 Hz, respectively, improved mixing, as the injection frequency component was no longer noticed in the effluent samples. The variable-flow nozzle was able to maintain appropriate carrier flow rates to achieve product label chemical concentrations. In one case, the maximum allowable concentrate was exceeded, although the nozzle was able to recover in 0.5 s. Steady-state errors ranged from 2.5% to 7.5% for chemical concentrations compared to the selected chemical to carrier ratio (0.03614). This test scenario represented an application rate of 4.68 L ha-1 with velocity increases from 4.0 to 7.1 m s-1 and decreases from 7.1 to 4.0 m s-1, which were typical of the example field application data

Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application

An electronic, resistance-based sensor array and data acquisition system was developed to measure spray deposition from hydraulic nozzles. The sensor surface consisted of several parallel tin plated copper traces of varying widths with varying gap widths. The system contained an embedded microprocessor to monitor output voltage corresponding to spray deposition every second. In addition, a wireless module was used to transmit the voltage values to a remote laptop. Tests were conducted in two stages to evaluate the performance of the sensor array in an attempt to quantify the spray deposition. Initial tests utilized manual droplet placement on the sensor surface to determine the effects of temperature and droplet size on voltage output. Secondary testing utilized a spray chamber to pass nozzles at different speeds above the sensor surface to determine if output varied based on different application rates or spray droplet classification. Results from this preliminary analysis indicated that manual droplets of 5 and 10 μL resulted in significantly different values from the sensors while temperature did not consistently affect output. Spray chamber test results indicated that different application rates and droplet sizes could be determined using the sensor array

A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples

Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected to visible and near-infrared spectrometers. A program was written that used reflectance data to determine the optimal pair of narrowband wavelengths to calculate a normalized difference water index (NDWI). Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples. Probe height above samples and moisture content were examined for statistical significance using the selected wavelengths. The effect of moisture was significant for both bare soil and wheat stalks, but probe height was only significant for wheat stalk samples. The index, when applied to all samples, performed well for soil samples but poorly for wheat stalk samples. Index calculations from soil reflectance measurements were highly linear (R2 \u3e 0.95) and exhibited small variability between samples at a given moisture content, regardless of probe height. Index calculations from wheat stalk reflectance measurements were highly variable, which limited the usefulness of the index for this material. Based on these results, it is expected that crop residues, such as wheat stalks, will reduce the accuracy of remotely sensed soil surface moisture measurements

Classifying Reflectance Targets under Ambient Light Conditions using Passive Spectral Measurements

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%