Linking genotypes to phenotypes is a key component of both fundamental and applied plant research. Recent advances in DNA sequencing technology has allowed large scale collection of genotypic information relatively easily and cheaply and has greatly outpaced advances in phenotyping.
This project focuses on the development of high-throughput phenotyping systems for measuring symptoms of the foliar disease, northern leaf blight (NLB), in field grown maize. Through a collaboration with Hod Lipson (Columbia University) and Rebecca Nelson (Cornell University), we are using aerial imagery collected via small unmanned aerial vehicles (sUAV) in combination with deep learning to recognize NLB symptoms in plant images. The ultimate goal is to develop a semi-autonomous system that can identify and quantify levels of NLB and other potential plant diseases in the field.
In addition to disease phenotyping, we are using aerial imagery to extract other agronomically important phenotypes such as canopy height, reflectance, and leaf area index.
Funding: NSF NRI IIS-1527232.