Technical Abstract: A majority of phenotypic variance in maize has qualitative aspects that are immeasurable by rulers or scalars. Image analysis may improve the phenotypic quantification by increasing the objectivity and granularity of quantification, which in turn may result in an increase in the rate at which the genes controlling phenotypic traits are isolated. This report describes the analysis of visible phenotypes for maize ears and leaves, using digitized images. This system handles images acquired in corn fields which, in contrast to images acquired in a greenhouse, present many processing challenges that stem from changing weather factors, shifting light and sub-optimal camera adjustments. The images acquired are first pre-processed and segmented. Then features are extracted and indexed in a high-dimensional space. Cluster indexed feature vectors have been used to score a disease, Southern Leaf Blight, characterize lesion mutants, and localize the captured phenotypes onto a genetic or physical map.