Data Science

During summer of 2023, I had the opportunity to intern with Impossible Sensing for eight weeks. During this time I explored different machine learning models to predict organic carbon in soil. Below is a summary of the work the project I worked on.

Laser induced breakdown spectroscopy (LIBS) has a variety of uses in space and deep ocean exploration. In this project, we focus on using LIBS and machine learning techniques to predict the amount of soil organic carbon (SOC) in soil samples. The final results of these models indicate that it is possible with some level of accuracy to predict the amount of SOC in soil using LIBS spectra. 

The focus of this project was to quantify the amount of soil organic carbon (SOC) using a laser induced breakdown spectroscopy (LIBS) instrument and machine learning techniques. Several machine learning algorithms were explored to better understand the possibility of predicting SOC from LIBS data. This work is meaningful to farming in a way that can assist farmers in maximizing the amount of carbon stored in their soil.