Informational and Electrical Technologies: Transforming ag and bio engineering

According to the Computing Research Association, Information technology has amplified our intellectual and physical abilities more than anything since the development of the written word ... We are hard-pressed to think of change of comparable magnitude in human history. While you may argue that other historical advances, such as the development and mechanization of agriculture, rival the advances currently seen in information and electrical technology IET), you cannot deny the effects that these ongoing technological developments are having on our lives today

Weed Sensing - Where Are We?

Recognition of the potential benefits of being able to variably apply herbicides based on the sensing of weeds has led to much research and development activity. The purpose of this paper is to survey what work has been done already with a view of formulating future research and development directions with a goal of the development of practical weed sensing technology. Two approaches have typically been used for weed detection. The first is the photo-detection approach, which measures the average reflected light from the field of view of the detector. Light-detecting (photo) diodes or resistors have been used in this low resolution approach meaning that the area sensed by one sensor is large. The other approach is the machine vision approach. In this approach, digital images of the field scene are acquired with some type of camera, and the information contained in these images is processed by a computer to retrieve knowledge or understanding of the scene. Examples of both of these approaches will be discussed in this paper

Parameter Sensitivity Analysis of a Tractor and Single Axle Grain Cart Dynamic System Model

Tractor and towed implement system models have become increasingly important for model-based guidance controller design, virtual prototyping, and operator-and-hardware-in-loop simulation. Various tractor and towed implement models have been proposed in the literature which contain uncertain or time-varying parameters. Sensitivity analysis was used to identify the effect of system parameter variation on system responses and to identify the most critical system parameters. Sensitivity analysis was performed with respect to three tire cornering stiffness parameters, three tire relaxation length parameters, and two implement inertial parameters. Overall, the system was most sensitive to the tire cornering stiffness parameters and least sensitive to the implement inertial parameters. In general, the variation in the input parameters and the system state variables were related in a non-linear fashion. With the nominal parameter values for a MFWD tractor, a single axle grain cart, and corn stubble surface conditions, a 10% variation in cornering stiffness parameters caused a 5% average variation in the system responses whereas an 80% change in cornering stiffness parameters caused an 80% average variation at 4.5 m/s forward velocity. If a 10% average variation in system responses is acceptable, the cornering stiffness parameters and implement inertial parameters must be estimated within 20% and 30% of actual/nominal values respectively. The relaxation length parameters have to be within 75% of the nominal values

Control and Evaluation Methods for Multi-Mode Steering

A self-propelled agricultural sprayer was modified to enable both front and rear wheel steering through electrohydraulic control valves. These modifications, in conjunction with a digital controller, enabled the vehicle to be four-wheel steered in multiple modes. The research focused on modeling and evaluating the effect of multi-mode four-wheel steering on vehicle handling characteristics and vehicle performance of the sprayer. The multi-mode steering system was evaluated by driving the sprayer through specified paths in the different steering modes. The position and heading of the vehicle were measured for each mode using two dual frequency DGPS receivers. From the measure of vehicle posture, sprayer performance measures such as over/underspray and crop damage were assessed for each steering mode. Preliminary results show that drivers were able to take advantage of added maneuverability in headland turning procedures. Crab steering reduced the amount of area sprayed in error during lateral course adjustments. The steering and vehicle models yielded similar responses to steering inputs as experimental responses

Modeling the Raven SCS-700 Chemical Injection System with Carrier Control with Sprayer Simulation

Mathematical models of the chemical and carrier control sub-systems of the Raven SCS-700 chemical injection system were developed. The step responses of both control sub-systems were predictable using the models. From this process of model development, it was observed that the voltage saturation effect limited the response speed of the carrier valve motor and the resulting speed at which changes could be made to the carrier flow rate. The rate at which flow rate measurements were available limited the extent to which valve motor speed could be increased without causing instability. The performance of three different types of sprayers was simulated using these models. Simulations were performed with and without carrier control. Sprayers using carrier control misapplied to smaller areas than those without carrier control. Differences between the response of the chemical and carrier sub-system controllers produced concentration variations that contributed to application error. These errors, however, were small relative to the errors caused by response times of both systems and the ground speed sampling rate

Open and Closed Loop System Characteristics of a Tractor and an Implement Dynamic Model

Accurate guidance of towed implements is important for performing agricultural field operations and for gaining the ultimate benefit from an agricultural automatic guidance system. The study of open and closed loop system responses can be helpful in the design of practical guidance controllers. A dynamic model of a tractor and a towed implement system was developed. Open loop analysis of the kinematic and dynamic models revealed that the dynamic model was essential for capturing the higher order dynamics of the tractor and implement system at higher operating velocities. In addition, a higher fidelity dynamic model was also developed by incorporating steering dynamics and tire relaxation length dynamics. Closed loop system characteristics were studied by using a linear quadratic regulator (LQR) controller. The tractor position and heading and implement heading states along with respective rate states were fed back to close the loop. The higher fidelity closed loop system used a practical range of steering angles and rates to keep the response within nominal off-road vehicle guidance controller design specifications in the forward velocity range of 0.5 m/s to 10 m/s (1.8 km/h to 36 km/h). These simulation studies provided understanding about the characteristics of the tractor and towed implement system and showed promise in assisting in the development of automatic guidance controllers

Targeted Sampling of Elevation Data Based on Spatial Uncertainty of Prior Measurements

An efficient sampling strategy should address knowledge gaps, rather than exhaustively collect redundant data. In this study, spatial uncertainty in DEM estimates was used to locate targeted sampling areas in the field. An agricultural vehicle equipped with RTK-DGPS was driven across a 2.3 ha field area to measure the field elevation. Data were collected at 3.05 m (10 ft) intervals in a continuous fashion at a speed of 9.6 mph. A geostatistical simulation technique was used to simulate field DEMs with different measurement pass intervals and to quantitatively assess the spatial uncertainty of the DEM estimates. The high uncertainty areas for each DEMs were classified using image segmentation methods and targeted sampling was performed on those areas. The resulting DEMs were compared with each other to evaluate the effect of including targeted measurement on DEM accuracy. The addition of targeted measurements significantly reduced the time dedicated for the re-sampling effort and resulted in DEMs with lower RMSE. For the widest interval between sampling passes, the RMSE of 0.46 m of the DEM was reduced to 0.25 m after adding the targeted measurements which was close to the 0.22 m RMSE of DEM with whole field re-sampling. The results show that spatial uncertainty models are useful to design targeted sampling for field mapping. The method is not limited to map elevation data but can be extended for mapping other spatial data

Development of Agricultural Field DEM Using Repeated GPS Measurements from Field Operations: Effects of Sampling Intensity and Pattern

Widespread use of GPS systems in agricultural vehicle allows farmers to collect elevation data repeatedly to develop field-level DEM. Accuracy of these DEMs can be improved by understanding how errors are introduced from sampling procedures. In this research, a 120 m by120 m test field was modeled using a 10-m-grid USGS DEM of Winneshiek County, Iowa. Multiple sets of vehicle-based GPS elevation measurements from four filed operations (tillage, planting, spraying and harvesting) with different swath width and speed level were simulated using inverse distance weighting (IDW) interpolation from the test field. GPS errors were modeled using Gauss-Markov process and added to the simulated measurements. Then DEMs were created using a method proposed by Aziz et al. (2005). Results show that RMSE gradually decreased as the number of measurement sets used increased and leveled out after approximately 12 measurement sets unless an increase in input resolution of the elevation data were introduced to improve the RMSE of the resulted DEM. For the widest swath width, as the speed level (distance between data points along a track) decreased, the RMSE decreased from 0.23 m to 0.16 m. Track patterns on the other hand, had significant effects on the topographic maps if very small grid size is used to generate the DEM

Automatic Corn Plant Population Measurement Using Machine Vision

From yield monitoring data, it is well known that yield variability exists within a field. Plant population variation is a major cause of this yield variability. Automated corn plant population measurement has potential for assessing in-field variation of plant emergence and also for assessing planter performance. Machine vision algorithms for automated corn plant counting were developed to analyze digital video streams. Video streams were captured along 6.1 m long cornrow sections at early stages of plant growth and various natural daylight conditions. A sequential image correspondence algorithm was used to determine overlapped image portions. Plants were segmented from the background using an ellipsoidal decision surface, and spatial analysis was used to identify individual crop plants. Performance of this automated method was evaluated by comparing its results with manual stand counts. Sixty experimental units were evaluated for counting results with corn population varying from 14 to 48 plants per 6.1 cornrow length. The results showed that in low weed field conditions, the system plants counts well correlated to manual counts (R 2 = 0.90). Standard error of population estimate was 1.8 plants over 34.3 manual plant count that corresponds to 5.4% of average error

Topographic Mapping Through Measurement of Vehicle Attitude

A self-propelled agricultural sprayer was equipped with four RTK DGPS receivers, and an Inertial Measurement Unit (IMU) to measure vehicle attitude and field elevation as the vehicle was driven across a field. Data was collected in a stop-and-go fashion at 3.05 m (10 ft) intervals, as well as in a continuous fashion at three different speed levels. Using ordinary kriging, surface grids were interpolated using only elevation measurement, as well as combinations of elevation and vehicle attitude measurements. The resulting surfaces were compared to each other to evaluate the effect of including attitude measurement on DEM (Digital Elevation Model) accuracy. At the widest row spacing, the DEMs generated with attitude measurements had lower RMSE than those DEMs generated without attitude measurements. Vehicle speed also affected DEM accuracy. Vehicle attitude measurements have the potential to improve DEM accuracy for larger swath widths in ordinary field operations