Date of Award
Master of Science
Agricultural and Biosystems Engineering
Matthew J. Darr
Over the next several decades the need for grain-based commodities will increase, due mostly to the demand for grain in developing countries and grain-based biofuels. To meet the increase in demand additional grain-based commodities will come from two likely sources: current land used for production and land yet to be developed. Increased spatial resolution of crop production is one way producers can grow more crops without more land development. If farmland productivity can be monitored on a smaller scale, producers can begin to implement better site-specific management decision at a higher resolution, such as variable-rate nitrogen application. The development of high resolution yield mapping techniques would provide producers the ability to evaluate if additional grain could be produced.
Currently, yield mapping technology provides an average yield value for a discrete harvested area. Average yield values are the summation of grain harvested by a number of row units across a corn-head. The harvested area is a function of the number of row units on a corn-head. The primary goal of this research was to develop an ear detection system that could predict the number of ears that entered individual row units. By generating ear count prediction for individual row units, harvested grain could be spatially allocated across multiple section of the corn-head to produce higher resolution yield maps.
Lensing, Keith Joseph, "Algorithm Development of a Multi-Section Crop Detection System for a Corn Head" (2015). Graduate Theses and Dissertations. 16537.