Improved quality of seeds, with techniques like genetic engineering, have helped farmers to improve food productivity. However, there has been an increase in illegal seed practices, including counterfeit seeds, fake seeds, fraudulent labelling, etc. The goal of my project is to develop machine-learning models for detecting counterfeit seeds with image processing and convolutional neural nets. In addition to this, I will help work on developing a Graphic User Interface platform for anti-counterfeiting solutions of seeds based on signal-processing algorithms and cryptographic algorithms.

Through SuperUROP, I hope to be able to gain more experience in applying machine learning techniques in a hands-on manner to help solve a meaningful issue in our world. I am excited to be able to apply my learnings from coursework and from being an LA for the intro ML class. I hope to be able to learn more about how research is conducted, and look forward to focusing my energy on creating an impactful project.