During the spring semester, in-person concerts, events and lectures that involve outside guests will not be held, per the university’s COVID-19 travel and visitor policy.
Monday, April 12, 2021 at 12:00pm to 1:00pmVirtual Event
COVID-19 update; this seminar is now zoom-only. Please use the zoom link: https://cornell.zoom.us/j/97636298817?pwd=a2ZLQlBqbHJ5dXJ1QjZpeEl3cjRpdz09 with password: cida
Early Pest Detection with the Applied Chemical Ecology Technology Program
Targeting pests and pathogens for management in agricultural systems relies upon accurate and early detection of these problems before they reach levels of economic damage. Due to the cryptic nature of many pests and pathogens, particularly those belowground, detecting and identifying these problems early is a challenge. We'll talk about two new technologies that hold tremendous promise for accurate and early identification of pests and pathogens above and belowground. The first is the use of volatile profiles. By collecting 'smells' and analyzing them on high-throughput analytical chemistry equipment, we can use supervised and unsupervised machine learning algorithms to identify management problems before they start. The second is the use of custom-engineered instrumentation that, when coupled with a trained machine learning pipeline, can separate, count, sort, and identify live organisms from raw soil samples in minutes per sample. For each of these approaches, we'll touch on the development of the technology, how it works, scaling, and its long-term impact.
Denis Willett is an Assistant Professor at Cornell AgriTech. His background is in chemical ecology, data science, and human-centered design. His work spans the spectrum of basic to applied research with a focus on developing solutions to agricultural pest management problems in New York and across the world.
The Cornell Institute for Digital Agriculture (CIDA), a faculty led initiative focused on creating a strong voice in the emerging area of Digital Agriculture (DA), invites Asst. Professor Willett to present his research for CIDA’s monthly seminar series.
Meeting ID: 976 3629 8817