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

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业领域木薯植株的感染状况。木薯等适应气候变化的作物预计将在21世纪的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会在茎和叶上造成轻微的症状或没有症状，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了，直到他们歉收。CBSD的细微症状使研究该疾病在田间的传播变得困难。该项目将首次使用MSI对CBSD的本地传播进行精确建模。除了改善撒哈拉以南非洲食用木薯的人的粮食安全外，这项技术和建模框架可能对其他营养繁殖作物的疾病有用，如土豆、红薯、芋和山药。此外，多国团队将培训本科生、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；然后通过训练木薯树叶扫描的机器学习模型来解释产生的光谱。在实验室条件下，当植物没有明显症状时，MSI在感染后28天检测CBSD感染的准确率为95%。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在试验田的传播情况，并对其传播进行建模，以评估在CBSD传播之前使用MSI检测并从田地中移除受感染的木薯的有效性。该项目将决定微卫星系统能否提高东非清洁种子系统的成功。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.