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

向日葵是价值200亿美元的油料作物，也是世界上主要的植物油来源之一。然而，向日葵可能是一种高风险作物，因为它可能会因疾病而损失。疾病管理计划的一个主要组成部分是早期病原体检测和立即实施控制策略，以尽量减少经济损失。监测和管理是非常劳动密集型的工作，当症状变得明显时，感染已经确立，从而降低了杀菌剂的功效。相比之下，基于传感器的自动诊断为传统的视觉筛查提供了强大的替代方案。在本项目中，我们将研究由Ecoation开发的高分辨率传感系统在向日葵中一些最重要疾病的抗性筛选中的实际应用：锈病、茎溃疡病（PSC）和菌核病（SHR）。我们还将把这项技术的效率与一些现有的替代方法以及最广泛使用的方法——目视筛查进行比较。这些知识将有助于开发更准确和有效的策略来检测和量化可用于精准农业的疾病严重程度。

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.