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The use of substances other than petroleum based fuels for power sources is not a new concept. Prior to the advent of petroleum fueled vehicles numerous other substances were used to create mobile sources of power. As the world's petroleum supply dwindles, alternative fuel sources are sought after to replace petroleum fuels. Many industries are particularly interested in the development of renewable fuel sources, or biologically derived fuel sources, which includes ethanol. The use of No. 2 diesel as well as many alternative fuels in compression ignition engines result in injector coking. Injector coking can severely limit engine performance by limiting the amount of fuel delivered to the combustion chamber and altering the spray pattern. Injector tip coking is also one of the most sensitive measures of diesel fuel quality [1]. A machine vision system was implemented to quantify injector coking accumulation when a compression ignition engine was fueled with oxydiesel.

Machine operators rely on intuition and experience to evaluate vehicle performance. As we increasingly turn to automation, it is important to automatically evaluate sensor data and system performance. Fuzzy logic allows us to take advantage of domain knowledge to evaluate data and to describe a system linguistically. In this paper, two automated fuzzy evaluation systems are described. In the first, a fuzzy quality module evaluates output from a simulated noisy sensor. In the second system, a fuzzy quality module evaluates the output from a machine vision system. Results from both systems indicate that fuzzy logic was able to accurately categorize the output in support of machinery decision making for automated control.

The development of automated guidance for agricultural tractors has addressed several basic and applied issues of agricultural equipment automation. Basic analyses have included the dynamics of steering systems and posture sensors for guidance. Applied issues have evaluated the potential of several commercial sensing systems and a commercial mechanical guidance system. A research platform has been developed based on a Case 7220 Magnum1 2-wheel drive agricultural tractor. An electrohydraulic steering system was used and characterized in support of automated guidance control. Posture sensing methods were developed using GPS, geomagnetic direction sensors (GDS), inertial, and machine vision sensing systems. Sensor fusion of GPS-inertial-machine vision and GPS-GDS-machine vision provided the most flexible and accurate guidance and capable for operation under dynamically changing field conditions.

A machine vision system was developed to identify and locate harvestable spears of asparagus. An image acquisition vehicle was fabricated to videotape portions of asparagus rows from a commercial production field. Images were acquired using a monochrome CCD camera. The detection of reflectance properties of asparagus was enhanced by using optical bandpass filters for near-infrared radiation. Videotaped segments acquired in the field were analyzed. Image processing techniques based on geometrical characteristics of asparagus spears were used to identify and locate harvestable spears in the images. Harvestable spears measured in the field were compared to those found by machine vision. The vision system correctly identified from 86 to 97% of the harvestable spears in six 15 m row segments analyzed. The uncertainty in the location of spears was within a 2.97 by 5.39 cm window with 95% confidence.

A heuristic line detection algorithm is described for computing guidance information from row crop images. The technique processes binary images representing crop rows against a soil background. Points along the centers of crop rows are enhanced using a modified run-length encoding procedure. The properties of lines in images can be improved by filtering based on characteristics of the object run-length. A clustering algorithm was used to aggregate pixels that fall on the same crop row. The technique was compared with the Hough transform, a common line detection technique in image processing. Both procedures accurately represented lines measured manually in a set of images representing a range of expected field conditions.

Image processing techniques were investigated for developing a guidance signal for a tractor operating on agricultural row crops. The guidance signal was computed from thresholded images segmented by a Bayes classifier. The distribution of crop canopy and soil background pixels in an image was approximated with a bimodal Gaussian distribution function. The parameters of the distribution were estimated by regression to systematically subsampled images. Run-length encoding was used to locate center points of row crop canopy blobs in the thresholded images. A heuristic line detection algorithm was used to determine the parameters defining crop row location on the image plane. Row parameters were used to compute a tractor guidance signal. Results are presented on the performance of the individual components of the algorithm.