A machine vision–based corn plant population sensing system was developed to measure early growth stage corn population. Video was acquired from a vehicle–mounted digital video camera at V3 to V4 stages under different daylight conditions. Algorithms were developed to sequence video frames and to segment, singulate, and count corn plants. Vegetation segmentation was accomplished using a truncated ellipsoidal decision surface. Two features were extracted from each pixel row of the segmented images: total number of plant pixels, and their median position. Adjacent rows of the same class were grouped together and iteratively refined for final plant counting. Performance of this system was evaluated by comparing its estimation of plant counts with manual stand counts in 60 experimental units of 6.1 m sections of corn rows. The number of corn plants in these experimental units ranged from 14 to 48, corresponding to a population of 30,000 to 103,000 plants /ha. In low–weed field conditions, the system plant count was well correlated to manual stand count (R2 = 0.90). Standard error of population estimate was 1.8 plants over 33.2 mean manual plant count, or 5.4% coefficient of variation.