FORAGES AND FEEDS: Original Research| Volume 36, ISSUE 4, P480-488, August 2020

Predicting in situ dry matter disappearance of chopped and processed corn kernels using image-analysis techniques



      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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