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

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.