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Corn is one of the major crops in the Midwest. In-field variations associated with corn plant spacing, growth stage, and population can lead to significant yield differences. In order to minimize these variations, seeds should be placed at a uniform spacing and at a desired seeding depth. Since the ability to achieve this uniformity is directly related to the planter performance, intensive field evaluations are vitally important before a prototype planter is marketed. A machine vision-based emerged corn plant spacing, growth stage, and population measurement system (ECS) was successfully developed for the performance evaluation for prototype planters. The system was capable of doing real-time image-sequencing with the distance error ratio of less than 1% under various wind and soil conditions.