Applying machine learning methods and analysis on remotely sensed color, multispectral, and thermal imagery has been recognized as a potentially cost-effective approach for detecting the location of various weed species in-field. This detection approach has the potential to be an important first step for broader Site-Specific Weed Management procedures (SSWM). The objective of this research was to create a method for automating the detection of weeds in corn and soybean fields, at different stages of the growing season. Sensors based on an unmanned aerial vehicle were used to capture imagery used for this research. We focused on identifying four common weed types present in Midwestern fields. This research involved: 1) collecting color, multispectral, and thermal imagery from UAV based sensors in corn and soybean fields throughout the 2018 growing season, 2) creating individual normalized differential vegetation index (NDVI) images from the near-infrared (NIR) and red multispectral bands 3) applying image thresholding and smoothing techniques on the NDVI imagery , 4) manually drawing bounding boxes and hand labelling vegetation blobs from the processed imagery using color images as the ground truth, 5) developing a training set of these processed, labeled images that represent weeds at different crop growth stages. Preliminary results of these methods show promise in creating an affordable first step to target herbicide application.