Crop yields are inherently limited by plant stresses (biotic and abiotic). Plant breeders have protected yield from plant stress losses by incorporating resistance genes and developing more resilient cultivars. State-of-the-art High Throughput Phenotyping has unlocked new prospects for field-based phenotyping. What is currently lacking is methodology to quickly screen HTP images into easy-to-use tools that help identify, detect, classify and predict plant diseases. This research aims to use hyperspectral camera and spectro-radiometer to develop disease signatures to distinguish among SCN, SDS, BSR, charcoal rot and IDC; develop algorithms to differentiate diseases with confounding symptoms; develop predictions for disease onset using "disease signatures” and develop an algorithm to count SCN eggs under the microscope rapidly and accurately manner.

Key Benefactors:
farmers, agronomists, Extension agents, soybean breeders, seed companies