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仪（MSI）技术的实际可用性，以快速确定坦桑尼亚农田中木薯植物的感染状况。木薯等适应气候变化的作物预计将在世纪粮食安全中发挥关键作用。然而，东非的木薯生产受到引起木薯褐条病（CBSD）的RNA病毒的限制。CBSD在茎和叶上引起轻微或没有症状，同时破坏根组织，这意味着农民通常不知道他们的田地被感染，直到他们收获失败。CBSD的微妙症状使得研究该疾病在田间的传播变得困难。该项目将首次使用MSI准确地模拟CBSD的局部传播。除了改善撒哈拉以南非洲食用木薯的人们的粮食安全外，这项技术和建模框架可能对其他蔬菜繁殖作物（如马铃薯，甘薯，芋头和山药）的疾病有用。此外，该跨国团队还将培训本科生、研究生和博士后学者，以解决全球互联农业研究中的问题。MSI的工作原理是使用许多不同的波长观察树叶;然后通过在木薯树叶扫描上训练的机器学习模型解释产生的光谱。在实验室条件下，MSI在感染后28天以95%的准确度检测CBSD感染，此时植物没有可见的症状。该项目将评估和改进MSI的性能，以检测田间CBSD感染，并将这些光谱与其他植物胁迫引起的光谱进行比较。该项目将测量CBSD在实验田中的传播，并对其传播进行建模，以评估在CBSD传播之前使用MSI检测和清除受感染木薯植物的有效性。该项目将确定MSI是否能够提高东非清洁种子系统的成功率。这些系统旨在分发无病种植材料，但在控制CBSD的传播方面存在问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。