  Model selection when designing deep learning systems for specific use-cases
can be a challenging task as many options exist and it can be difficult to know
the trade-off between them. Therefore, we investigate a number of state of the
art CNN models for the task of measuring kernel fragmentation in harvested corn
silage. The models are evaluated across a number of feature extractors and
image sizes in order to determine optimal model design choices based upon the
trade-off between model complexity, accuracy and speed. We show that accuracy
improvements can be made with more complex meta-architectures and speed can be
optimised by decreasing the image size with only slight losses in accuracy.
Additionally, we show improvements in Average Precision at an Intersection over
Union of 0.5 of up to 20 percentage points while also decreasing inference time
in comparison to previously published work. This result for better model
selection enables opportunities for creating systems that can aid farmers in
improving their silage quality while harvesting.
