US-UK Collab: Resurrecting a role for roguing: Presymptomatic detection with multispectral imaging to quantify and control the transmission of cassava brown streak disease

美英合作：恢复欺诈行为：利用多光谱成像进行症状前检测，以量化和控制木薯褐条病的传播

• 批准号: 2308503
• 负责人: Siobain Duffy
• 金额: $ 218.02万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308503&HistoricalAwards=false
• 关键词: US; UK; Collab; Resurrecting; role

This project will test the practical usability of newly-developed multispectral imager (MSI) technology to rapidly determine the infection status of cassava plants in agricultural fields in Tanzania. Climate change-resilient crops like cassava are projected to play a key role in 21st century food security. However, cassava production in East Africa it is limited by RNA viruses that cause cassava brown streak disease (CBSD). CBSD causes subtle or no symptoms on stems and leaves, while destroying the root tissue, which means farmers are often unaware their field is infected until they have a failed harvest. The subtle symptoms of CBSD have made it difficult to study the spread of the disease in fields. This project will use the MSI to accurately model the local spread of CBSD for the first time. In addition to improving the food security of people who eat cassava in sub-Saharan Africa, this technology and modeling framework may be useful for diseases of other vegetatively propagated crops such as potato, sweet potato, taro, and yam. In addition, the multinational team will train undergraduates, graduate students, and post-doctoral scholars to tackle problems in globally-connected agriculture research.The MSI works by observing leaves using many different wavelengths; the resulting light spectra are then interpreted by machine learning models trained on cassava leaf scans. Under laboratory conditions, the MSI detects CBSD infection with 95% accuracy at 28 days post infection, when plants have no visible symptoms. This project will assess and improve the performance of the MSI to detect CBSD infection in the field and to compare those spectral to ones caused by other plant stresses. The project will measure the spread of CBSD in experimental fields, and model its spread to evaluate the efficacy of using the MSI to detect and remove infected cassava plants from fields before CBSD can spread. The project will determine whether the MSI can improve the success of clean-seed systems in East Africa. Those systems aim to distribute disease-free planting material, but have had problems controlling the spread of CBSD.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将测试新开发的多光谱成像器（MSI）技术的实际可用性，以迅速确定坦桑尼亚农业领域木薯植物的感染状态。木薯这样的气候变化 - 预计将在21世纪的粮食安全中发挥关键作用。但是，东非的木薯生产受到引起木薯棕色条纹疾病（CBSD）的RNA病毒的限制。 CBSD会在茎和叶上引起微妙或没有症状，同时破坏根组织，这意味着农民通常不会意识到自己的田地被感染，直到收获失败为止。 CBSD的微妙症状使得很难研究该疾病在领域的传播。该项目将使用MSI首次准确地对CBSD的局部扩散进行建模。除了改善在撒哈拉以南非洲吃木薯的人的粮食安全外，该技术和建模框架可能对其他营养繁殖作物（例如马铃薯，红薯，芋头和山药）的疾病有用。此外，跨国公司将培训本科生，研究生和博士后学者，以解决全球农业研究中的问题。MSI通过使用许多不同的波长观察叶子来探讨叶子的作品；然后，通过木薯叶扫描训练的机器学习模型来解释所得的光谱。在实验室条件下，当植物没有明显的症状时，MSI在感染后28天以95％的精度检测到CBSD感染。该项目将评估和改善MSI的性能，以检测现场的CBSD感染，并将这些光谱与其他植物应力引起的光谱进行比较。该项目将测量CBSD在实验领域的扩散，并建模其扩散，以评估使用MSI检测和去除CBSD传播之前从田野中检测和去除感染的木薯植物的功效。该项目将确定MSI是否可以改善东非清洁物质系统的成功。这些系统旨在分发无病的种植材料，但在控制CBSD的传播方面遇到了问题。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。