Honors College Thesis
 

Machine Learning and Optimization Applications in Agriculture

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https://ir.library.oregonstate.edu/concern/honors_college_theses/k930c467m

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  • The ever-increasing global population presents looming problems for the field of agriculture. Global food demand will, at some point, increase to the point where there is not enough crop-ready land to keep up. This creates an additional incentive, other than economics, for growers to increase their yield-per-acre and make sure that the maximum possible amount of food that they are producing reaches consumers. In recent years computer assistance has been increasingly utilized by growers to make crop-related decisions. This study introduces two computer-aided methods of decision making in agriculture. First, this study introduces a method for predicting the yield of progeny maize plants given sparse yield data taken from the plant's parents. Next, this study introduces an optimization model which generates planting schedules that lead to consistent harvests which are close to the amount a location's infrastructure can handle.
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