COVID-19 update; this seminar is now zoom-only. Please use the zoom link:  https://cornell.zoom.us/j/94133933183?pwd=OWJpamlrMjNuRmlweTBDZnZhVlUvdz09 with password: cida

Relatively recent advances in unmanned aerial systems (UAS), or drone technology, as well as miniaturization of complex remote sensing systems, have enabled novel approaches to precision agriculture. Specifically, imaging spectroscopy (hyperspectral), light detection and ranging (lidar), and structure-from-motion (SfM; photogrammetry) can be used for agricultural disease detection, structural quantification, moisture stress assessment, and nutrient mapping. This talk will focus on RIT and collaborator Cornell University’s efforts to develop robust analytical approaches to a range of precision agriculture challenges. We will highlight efforts to develop models for i) proactive management of disease (white mold; Sclerotinia sclerotiorum), ii) precise harvest scheduling, and iii) yield prediction, all for our snap bean proxy crop, as well as highlighting ongoing work on yield mapping (corn) and vineyard nutrient assessment.

Jan van Aardt is a professor in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology, New York. Imaging spectroscopy and structural (lidar) sensing of natural resources form the core of his efforts, which vary between vegetation structural and system state (physiology) assessment. Or stated differently, the interaction between photons and leaves is what really gets him going. He has received funding from NSF, NASA, Google, USFS, and USDA, among others, and he has published >80 peer-reviewed papers and >80 conference contributions.

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 Professor van Aardt to present his research for CIDA’s monthly seminar series.