Capstone Group11 Green Team final version code

Currently, OSU seed analysts observe 25,000 grass seeds for each sample, pulling out by hand all of the off-types. A seed analyst may perform this on ten different samples per day, which causes eyestrain, headaches, backaches and tends to be boring. Currently there are machines that can identify larger seeds, like cereals (wheat, oats and rice) and separate the off-types. The challenge is that the grass seed market is a bit smaller so no one has developed a machine. The machine developed with this project could be the first to do so for grass seed, which would make the seed analysts more efficient in their jobs. If done correctly, it could also improve the accuracy of the final report and change the seed testing industry.

Seed Identification using computer vision, machine learning, Jetson TX2 processor and high a resolution camera to identify seed and output a signal.