Category: Automation and High-Throughput Technologies
Corn silage is a popular forage for ruminant animals because of its high energy and digestibility. Corn grain is often added to ration formulations to improve nutritional balance. A softer kernel or a hybrid that is harvested when it is less mature is easier to digest in the rumen. The fl2 floury mutation produces kernels with soft and starchy endosperm with high lysine content. Adding grain with the fl2 mutation to rations may reduce the amount of grain needed for nutritional requirements and reduce the need for kernel processing at harvest. The end goal is to produce maize hybrids that that have increased starch digestibility compared to wild type, and thus add value to animal feeding.
Currently fl2 can be assessed in two ways: manual inspection using a light table to select opaque fl2 kernels, or the use of a genotypic marker. The light table screening requires a trained technician to look into a bright light for long periods of time, causing ergonomic stress. The sorting efficiency of the manual method varies from site to site, as technician subjectivity plays a role. The genetic selection method is very accurate but also resource intensive, as it requires the kernels to be planted and grown then sampled to perform analysis.
We have developed a prototype sorting machine that sorts kernels exhibiting floury characteristics from a bulk sample with high accuracy and throughput. This is accomplished by mechanically singulating and imaging kernels, scoring them using a custom vision algorithm, then binning the kernels in the appropriate reservoir. The machine reduces operator ergonomic stress and allows for walk-away operation, as well as increasing accuracy from the current manual process. This machine will allow for a more accurate and cost effective method to develop floury hybrids.
Gregori Temnykh– Automation Engineer, Dow AgroSciences, Indianapolis, IN