ABSTRACT
Objective
Corn silage processing score (CSPS) is a well-known and often-used indicator of starch
availability in whole-plant corn silage. However, obtaining results from a laboratory
can take days or more. The objective of this work was to test an image-analysis method
as a tool for quantitative assessment of corn kernel particle size and feed quality
during harvest.
Materials and Methods
Kernel processor gap settings of 1, 2, 3, and 4 mm were assessed using the standard
sieving method and with an image-analysis method. In situ slowly disappearing DM in
ruminally cannulated lactating dairy cows was also assessed at the various kernel
processor gap settings and compared with the 2 CSPS estimation methods: image analysis
and sieving.
Results and Discussion
The image-analysis method was able to statistically separate mean estimated CSPS (P = 0.014) across the different crop processor gap settings for fresh samples. Image
analysis CSPS estimation of fresh samples was highly correlated with in situ DM disappearance
results [r(10) = 0.77] using a 12-h incubation time. These results indicate that image
processing is a viable tool for estimating CSPS and feed quality.
Implications and Applications
A smartphone application, SilageSnap, was developed that uses the image-processing
algorithm to estimate CSPS. This novel tool provides in-field estimation of CSPS that
translates into actionable information regarding feed quality to inform machine adjustment
decisions, which farmers, dairy nutritionists, and custom harvesters have not had
in the past.
Key words
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Article info
Publication history
Accepted:
June 14,
2020
Received:
February 7,
2020
Footnotes
The authors declare no conflicts of interest.
Identification
Copyright
© 2020 American Registry of Professional Animal Scientists. Published by Elsevier Inc. All rights reserved.
