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From yield monitoring data, it is well known that yield variability exists within a field. Plant population variation is a major cause of this yield variability. Automated corn plant population measurement has potential for assessing in-field variation of plant emergence and also for assessing planter performance. Machine vision algorithms for automated corn plant counting were developed to analyze digital video streams. Video streams were captured along 6.1 m long cornrow sections at early stages of plant growth and various natural daylight conditions. A sequential image correspondence algorithm was used to determine overlapped image portions. Plants were segmented from the background using an ellipsoidal decision surface, and spatial analysis was used to identify individual crop plants. Performance of this automated method was evaluated by comparing its results with manual stand counts. Sixty experimental units were evaluated for counting results with corn population varying from 14 to 48 plants per 6.1 cornrow length. The results showed that in low weed field conditions, the system plants counts well correlated to manual counts (R 2 = 0.90). Standard error of population estimate was 1.8 plants over 34.3 manual plant count that corresponds to 5.4% of average error.