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Assessing Kernel Processing Score of Harvested and Processed Corn Silage Via Image Processing Techniques
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1800888.(doi:10.13031/aim.201800888)
Authors: Jessica L Drewry, Brian D Luck, Rebecca Willet, Eduardo Rocha, Joshua Harmon
Keywords: kernel processing, kernel processing score, forage, image processing, silage
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.
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