Animal Health Sciences start-up was seeking a subject matter expert to develop a machine vision solution to determine the orientation of a baby chicken's head for the application of vaccine.
For this SBIR Phase 1 project, a custom lighting and optical solution were developed to acquire images of baby chicks as they passed under the camera. 1000 images were collected and annotated, 2/3rd of the images were then fed into Deep Learning algorithms. Detection performance was 97%, with an execution time of 19-22ms.
Seeds are collected and manually placed into a 96-well plate with gaps left open for control samples. These blocks enter into a machine that crushes the sends, which are then transferred over to another machine for genetic testing. The process of manually placing the seed in most, but not all wells of the test plate, is tedious and prone to mistakes. Also, it is important that one and only one seed is deposited in the appropriate well.
Seeds were dispensed into a small backlit bowl, A camera identified a single seed, with sufficient free space around it, so that a pick and place robot with a vacuum tip can move the seed from the bowl into one of the test wells of the 96-well plate.
A secondary camera, verified only one seed made it into the well of the plate. Certain seeds were prone to clog or gum up the surface of the vacuum tip and the secondary check verified compliance.