My research uses field images to predict crop yield, leveraging machine learning techniques to extract patterns and features correlating yield.  These features include plant health indicators, growth stages,  or canopy coverage. I am particularly interested in using these features to develop models  that improve the accuracy of yield prediction, helping farmers make  data-driven decisions. My approach considers temporal changes in the crop, capturing how its characteristics evolve. My work contributes to precision agriculture, a field that seeks to optimize resource use, increase productivity, and promote sustainability in farming. My research has the potential to transform traditional agricultural practices by integrating advanced AI methods.

Chocolate production is declining due to climate change and disease, threatening global supply. Ecuador’s cacao variety CCN-51, created by Omero Castro Zurita in 1965, offers a disease-resistant, high-yield solution. This MFA documentary project highlights his overlooked legacy and investigates whether CCN-51 can sustainably address the global cocoa shortage.

The research promotes interseeding—planting cover crops alongside cash crops—to help farmers in short-season climates protect soil, retain nutrients, and boost resilience. By identifying optimal planting times, crop mixes, and methods, the work dispels myths about competition and shows that interseeding can improve yields and soil health without compromising crop quality.