Abstract: Abstract: The missing amount of planted corn seedlings plays an important role in corn yield, to acquire it automatically, a new system based on machine vision has been developed. System hardware includes: one Industrial Personal Computer, a Central Processing nit: Intel (r) CPU i5@3.4GHz, 4 Gb of memory, one mvc3000 high speed Industrial camera (24FPS), and one Pentax len 8.5 mm f/1.5. The software development environment includes: Win7 Operating System, Microsoft Visual Studio 2010 Professional, and OpenCV1.0. The core of the system is the image processing method. Firstly, image sequences obtained along plant rows from a top view under in-field lighting conditions were registered to the uniform coordinate system. Secondly, plant pixel (vegetation) was segmented from the background with a pixel classifier trained by a neural network. The segmentation method employed a decision surface in color space that was defined by only three parameters. This surface was a a truncated ellipsoidal surface which was robust in outdoor field images under varying lighting conditions. A simple parallel algorithm working on 8-connectivity was implemented, whereby skeletonization extracts a network of thin curves that describe the overall shape or "skeleton" of objects in a binary image. Due to limitations in camera resolution and non-ideal lighting conditions, the minimum gray level point along the plant skeleton is the best estimation of the actual stem location. The minimum gray pixel area was searched along the plant skeleton, and the center of minimum gray pixel area was marked as the stem center. Finally, a plant row line was fitted by stem centers; a model that predicts a linear relationship between the stem centers and the corn plant row was defined, and the parameters of linear function was estimated by a least-squares fit. Stem centers were projected onto the row line, and the average plant spacing was calculated by a projected point. The number of missing plants between two neighbored seedlings has a linear relationship of plant average spacing. On three varieties of 10 repeats each, a 10 m long row field experiment was performed, In a low density experiment, measurement results of the method agree with manual measurements of 7 in 10 and 3 in 10 have a difference of one plant. In a high density experiment, measurement results of the method agree with a manual measurement 6 in 10 and 4 in 10 have a difference up to two plants. Comparison with a manual measurement and our method, a high correlation on the two methods was found; this method can replace manual measurement, reduce time cost and human labor effort, and improve the degree of automation of the corn seedling missing survey.