Stephen W. Searcy

A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas.

This study formulates a model to maximize the profit of a lignocellulosic biofuel supply chain ranging from feedstock suppliers to biofuel customers. The model deals with a time-staged, multi-commodity, production/distribution system, prescribing facility locations and capacities, technologies, and material flows. A case study based on a region in Central Texas demonstrates application of the proposed model to design the most profitable biofuel supply chain under each of several scenarios. A sensitivity analysis identifies that ethanol (ETOH) price is the most significant factor in the economic viability of a lignocellulosic biofuel supply chain.

Vision-based guidance of an agriculture tractor

The ordered structure of agricultural row crops can provide useful guidance information for tractor control. A description of research for coupling a machine vision system and a solid-state camera to derive vehicle guidance parameters for a tractor is presented. Image segmentation is enhanced by optical filtering and controlling light intensity to the image sensor. An analysis of camera location and steering errors that can be determined from the row crops is determined by simulating the geometric relationships between the crop canopy and the image plane.

Mapping of Spatially Variable Yield During Grain Combining

ABSTRACT An Allis-Chalmers N6 combine was instrumented with a grain flowmeter and location detection equipment. The installed data acquisition system was used to develop grain yield maps that showed in-field variations as a function of location in the field. An analysis methodology was developed to produce the maps by smoothing the location and grain flowrate data and reconstructing the yield from the flow into the combines grain tank.

VNIR DIFFUSE REFLECTANCE SPECTROSCOPY FOR AGRICULTURAL SOIL PROPERTY DETERMINATION BASED ON REGRESSION-KRIGING

Visible and near-infrared diffuse reflectance spectroscopy has been widely applied in precision agriculture to develop soil property prediction models. This method assumes that residuals of prediction are independently and identically distributed. However, this assumption is violated by spatial dependence common in soil samples collected from agricultural fields, and subsequent prediction models are usually sub-optimal. In this article, the regression-kriging method was used to account for spatial dependence among soil samples and aid in prediction model development. A total of 273 soil samples were collected from an agricultural field in Quitman County, Mississippi. Particle size distribution (clay and sand) and chemical analysis (Ca, K, Mg, Na, P, and Zn) were performed in the laboratory. Soil reflectance spectra were measured with a spectroradiometer (250 to 2500 nm). Soil samples were divided into two groups: 245 samples in the calibration set, and 28 samples in the validation set. The calibration set was first used to develop the principal component regression (PCR) models for each soil property. Semivariance analysis of prediction residuals from PCR revealed strong spatial dependence in Na; medium spatial dependence in Ca, Mg, and sand; weak spatial dependence in K and P; and a pure nugget effect in Zn and clay. Fitted theoretical semivariograms were then used to develop the regression-kriging models. Both the PCR and regression-kriging models were tested with the validation set, and their prediction capability was evaluated by R2 and RMSE (root mean squared error). The results showed that the only two soil properties that could be predicted by the PCR models were Mg (R2 = 0.4 and RMSE = 25.4%) and Ca (R2 = 0.33 and RMSE = 16.6%). On the other hand, the regression-kriging models were able to predict most soil properties with reasonably high R2 (reaching 0.65) and low RMSE. Most impressively, substantial increases of R2 and decreases of RMSE were achieved by the regression-kriging models for Na (R2 = 0.65 and RMSE = 29.0%, compared to R2 = 0.10 and RMSE = 44.4% in the PCR model) and sand (R2 = 0.49 and RMSE = 19.8%, compared to R2 = 0.06 and RMSE = 26.0% in the PCR model). It is anticipated that the proposed method could be integrated into GIS packages for various precision agriculture applications, such as digital soil mapping based on remotely sensed hyperspectral images.

Microcomputer-based tractor performance monitoring and optimization system

Abstract A tractor performance monitoring and optimization project was conducted in the United States to document tractor use on commercial farms and to improve tractor fuel efficiency. A two-wheel drive diesel tractor was instrumented to measure engine load, engine speed, wheel slip, fuel consumption, draught, and hitch forces. An on-board microcomputer monitored and recorded tractor performance. The microcomputer could also optimize tractor performance by recommending to the operator the optimum gear and throttle setting to achieve maximum fuel efficiency. Commercial farmers operated the instrumented tractor on their farms just as they operated their own tractors. Farmers typically ran the tractor at full throttle under light to moderate loads. Analysis indicated that farmers could have reduced fuel consumption 15–27% by practising “shift-up, throttle-back”; i.e. by shifting to a higher transmission gear and reducing the engine speed to maintain a nearly constant forward travel speed. Actual fuel consumption dropped from 11·3 to 20·0% in controlled field tests using a tractor operator information feedback system.

Measurement of agricultural field location using microwave frequency triangulation

Abstract The permanent mapping of soil or crop variability or the control of field operations in response to previously measured variabilities requires a method of absolute position determination. This article reports on research on the testing and use of a nautical microwave distance measuring unit for agricultural field location. It was found to be successful for providing the path plot of a combine. A theory and methodology for the optimum locations of repeater remotes for triangulation equipment was developed and demonstrated. The theory minimized the maximum fix error in a two-dimensional field by indicating proper placement of the remotes.

Soil water estimation using electromagnetic induction

Two published salinity models (designated the Rhoades and Mualem-Friedman models, respectively) were examined for application to real-time soil water estimation using apparent soil electrical conductivity. Field data were collected at two sites representing a range of soil types in central Texas: high shrinking-swelling Vertisols in Temple (the Heiden Clay site) and clay loam soils at the Texas A&M University Research Farm near College Station (the Westwood Scl site). The Rhoades-Corwin model developed for the Heiden Clay site yielded an R2 of 0.72 following calibration, predicted soil water within ±0.02 g g-1 during validation, and was deemed generally applicable for real-time soil water estimation. The Rhoades-Corwin model developed for the Westwood Scl site gave an R2 of 0.65 following calibration but could not be validated at the site and therefore was not considered applicable for real-time soil water estimation. A modified version of the Rhoades-Corwin model yielded a calibrated R2 of 0.91 at the Westwood Scl site with validation predictions within ±0.02 g g-1. The Mualem-Friedman model predicted soil water within ±0.05 g g-1 at the Heiden Clay site and was considered appropriate for real-time soil water estimation. At the Westwood Scl site, the Mualem-Friedman model could not be evaluated since saturation data were not available. Both models show promise for use for real-time, non-invasive soil water content estimation using apparent electrical conductivity, but additional testing is needed.

Computer vision determination of the stem/root joint on processing carrots

Two algorithms for analysing digital binary images and estimating the location of stem/root joints in processing carrots were developed. The first method was based on an analysis of carrot outlines, and was referred to as the slope algorithm. The second method utilized a priori knowledge of diameter relationships in the area near the stem/root joint, and was called the midpoint algorithm. The accuracy, consistency, and operating speeds of the two algorithms were measured to determine the feasibility of using computer vision as part of an automated crown trimming device. Both algorithms were capable of estimating the location of the stem/root joint with an error standard deviation of approximately 5 mm. The midpoint method was considered superior due to its faster execution time and better consistency. A system implementing the midpoint algorithm could feasibly attain speeds exceeding ten carrots per second.

Runoff Water Quality Impact of Variable Rate Sidedress Nitrogen Application

In recent years, precision agriculture has received attention from producers, agribusiness, and governmental agencies in an effort to increase profitability and protect the environment. Many aspects of precision agriculture, such as soil fertility, application technology, and economic factors, have received substantial research attention; however, other aspects of precision agriculture have not been well documented. One important issue that warrants increased attention is water quality. Because of precision application technology, variable rate fertilizer application based on within-field heterogeneity has the potential to decrease negative water quality impacts. Therefore, the objective of this paired watershed study was to evaluate the impact of variable rate nitrogen (N) fertilizer application on surface water quality. The variable rate field was divided into management units designated as poor, moderate, and high based on measured yield potential and received 100–160 kg/ha of N fertilizer. A portion of the N application was uniformly applied pre-plant or at planting, and rest was sidedressed at variable rates. The uniform rate field received uniform N application at 135 kg/ha. Surface water runoff and water quality were monitored for each field, and collected samples were analyzed for N and phosphorus (P) constituents. During the 2-year monitoring period with 22 storm sampling events, variable rate N application resulted in few water quality differences compared to uniform rate application, but overall median NO3 + NO2–N concentrations were significantly lower for the variable rate field in the second year of variable rate N application. Overall and event mean NO3 + NO2–N concentrations from the variable rate field tended to be higher, but median concentrations from the uniform rate field tended to be higher.

Estimation of tip shape for carrot classification by machine vision

Tip shape has been identified as an important carrot feature which is a major concern to both consumers and in post harvest operations. A classification method can help carrot breeders to measure the success of their breeding operations. Based on the Freeman chain code, a curvature profile was developed. Using a non-linear least squares technique known as the Marquardt method, the curvature profile was reduced to six parameters describing the carrot tip. These parameters were used to develop a Bayes decision function which classified carrot tips into five classes (sharp tapered to extremely blunt tips). This method was tested on 250 carrots. Of the 250 carrots tested, 14% were misclassified.