Non-invasive method of biotic and abiotic stress detection in sunflowers

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Sunflower in a $20 billion oil crop and one of the world's main sources of vegetable oil. However, sunflowercan be a high-risk crop because of potential losses from diseases. A major component of disease managementprograms is early pathogen detection and immediate implementation of control strategies to minimizeeconomic loss. Monitoring and management is very labor intensive and by the time the symptoms becomevisible, the infection has become well-established, diminishing the efficacy of fungicides. In contrast,automated sensor-based diagnostics provides a powerful alternative to conventional visual screening. In thisproject, we will examine the practical utility of high-resolution sensory systems developed by Ecoation forresistance screening of some of the most important diseases in sunflower: rust, Phomopsis stem canker (PSC)and Sclerotinia head rot (SHR). We will also compare efficiency of this technology to some of the availablealternatives, as well as to most widely used method - visual screening. This knowledge will enable thedevelopment of more accurate and efficient strategies for detecting and quantifying disease severity that couldbe used for precision agriculture.