Precision agriculture has the ability to provide knowledge to enhance grower decisions in peanut (Arachis hypogaea L.) and when used correctly, can contribute to increased profits and efficiency. Five primary objectives were created to contribute to this. A methodology was successfully developed to irradiate peanut seed to simulate non-uniform poor stand in the field. Additionally, a model was created to accurately identify plant material using aerial imagery collected from an unmanned aerial system in a field two to three weeks after planting. With the coupling of an economical threshold, this model can decrease bias in replant decisions. Thirdly, to identify effects of geographical location and planting date on yield, a survey was conducted that collected information regarding production methods and yield of peanuts in Georgia. Next, a method was explored to predict crop quality, as a measure of peanut grade, two weeks from harvest. Aerial images were collected of fields before harvest and were used to evaluate vegetation indices (VIs) for correlations to crop quality and yield. Results indicated that VIs could be beneficial to industry in predicting the quality of peanuts being harvested. Lastly, plant growth regulators also have the potential to increase yield and profits, however the high cost of these chemicals is a limiting factor in its use in peanuts. A trial was conducted to investigate physiological changes in the plant when prohexadione calcium is applied. Results showed differences in fluorescence and pigment content in plants treated with prohexadione calcium when compared to the non-treated check. The precision agricultural techniques explored in this dissertation have the ability to increase productivity and quality in peanut production at the farm and field level.