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A transformation of agriculture reached commercial reality at the beginning of this century as automated steering of agricultural machine systems increased the productivity and convenience in crop production systems. Following guidance, additional technologies have resulted in increasing optimized machine productivity. Today, integrated worksite solutions through machine and information management continues to transform agriculture. This is the precursor to autonomous worksite solutions that lead to the optimization of the worksite ecosystem. This paper will review the progress from the perspective of the customer value provided by increasing automated systems and the industry execution of autonomous driving technologies and will enable the pathways to autonomous worksites.

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.