Precision farming relies on the cost effectiveness of collecting and interpreting data, which describes the variations of agricultural conditions such as crop stresses, nutrient deficiencies, water stresses, or pest infestation. Hyperspectral remote sensing from satellites and airborne sensors can be a way to obtain data needed to develop site-specific farming management strategies. The primary objective of the hyperspectral applications in precision farming is to provide farmers with a technology, which can detect specific crop conditions that can be used to program variable-rate applications. Applications of water, pesticides, and fertilizer can be tailored to the needs of the agricultural crops, based on the conditions reflected on the imagery. This paper presents an experimental study performed in Beltsville, Maryland for assessing the plant density and nutrient uptake of corn using a simple photographic method from a model airplane versus obtaining hyperspectral imagery from an airborne sensor. The hyperspectral sensor utilized in this study was the AISA sensor. These remote sensors can measure the temperature of plants; or to be more specific, they can measure how much energy plants emit at the visible and near-infrared wavelengths of the spectrum, such as water and vegetation.