Predicting Kernel Processing Score of Harvested and Processed Corn Silage via Image Processing Techniques
2019
Drewry, Jessica L. | Luck, Brian D. | Willett, Rebecca M. | Rocha, Eduardo M.C. | Harmon, Joshua D.
An image processing algorithm was developed to characterize the size distribution of corn kernel particles from Whole Plant Corn Silage (WPCS). The algorithm determines particle cross sectional area and maximum inscribed circle diameter as well as cumulative undersize percent, dimensions of significance, and key characteristics of the distribution including mean particle size, skewness, and kurtosis. Kernel Possessing Score (KPS) was derived from the area-weighted cumulative undersize percent of 4.75 mm. Samples of WPCS harvested using self-propelled forage harvester with crop processing roll gap clearance of 1, 2, 3, and 4 mm were analyzed in their fresh, dry, and sieved states. Algorithm results were compared with the standard method of mechanical sieving and found to be well correlated r(23)=0.8, p<0.001. Additionally, analysis of particles before and after sieving indicated that sieving significantly increased KPS for 1 (t(5)=6.6, p=0.001), 2 (t(5)=4.2, p=0.01), and 3 (t(5)=3.5, p=0.02) mm samples. This algorithm has the potential to more accurately determine KPS in field compared to currently available methods, allowing for adjustment of kernel processing during harvest which will improve silage quality.
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