Zoom Recording ID: 94133933183
UUID: ErQAwniQTdevm3rcAYUYPA==
Meeting Time: 2021-04-12T15:48:30Z
Cornell Institute for Digital Agriculture - Spring 2021 Seminar: Digital Agriculture for Sustainable Farming
Early
Pest Detection with the Applied Chemical Ecology Technology Program
Denis
Willett, Assistant
Professor, Entomology, Cornell AgriTech
Abstract:
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.
Bio:
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.