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Abstract. An image processing algorithm was developed to identify and measure the diameter and area of corn kernel particles from whole plant corn silage. Additionally, the algorithm determines an area-weighted Kernel Processing Score (KPS) of the kernels within each image. Samples harvested using self-propelled forage harvester with kernel processor gap clearance of 1, 2, 3, and 4 mm were analyzed. Algorithm results were compared with the standard method of drying and sieving and found to be well-correlated r(11)=0.87, p<0.001. Additionally, analysis of particles before and after sieving indicated that sieving increased KPS for 1, 2, and 3 mm samples. This algorithm has the potential to determine KPS in-field, allowing for adjustment of kernel processing during harvest to improve silage quality.