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A major cause of corn yield variation within a field is plant population variation. An early plant population sensing system has potential as a tool for evaluating spatially varying emergence patterns and planter performance. Machine vision technology for automatically sensing early growth stage corn plant density and spacing was devel-oped in this research. Correspondence between sequential field images taken from a digital video camera was determined using pattern matching of intensity images. A color segmentation algorithm was used to separate plants from background. A neural network (NN) approach was used to correlate the green region in RGB color space to different lighting conditions. Plants were singulated based on plant object shapes. The NN approach was effective in adjusting the segmentation decision surface based on general measures of lighting changes.