Presymptomatic detection with multispectral imaging to quantify and control the transmission of cassava brown streak disease

确保80亿人的粮食安全是21世纪最紧迫的挑战之一。木薯等能够抵御干旱并在营养不良的土壤中生长的保险作物预计将在这些努力中发挥关键作用。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在破坏根部组织的同时，在茎和叶上引起轻微或没有症状，这意味着农民可能不知道他们的田地被感染了，直到他们收获失败。令人不安的是，可见的症状是如此轻微，由“清洁种子”计划提供的插条可能会被感染。虽然像PCR这样的分子诊断可以确定植物是否受到感染，但它们在经济上无法测试田间的许多植物。由于没有治愈CBSD的方法，农民只能欺骗受感染的植物（在它们传播感染之前将它们从田间移除），但基于可见症状的胭脂模型显示该方法无效。由于在收获前很难观察感染，我们不知道CBSD在田间传播的速度有多快，因为它与粉虱媒介的短暂联系损害了建模工作。CBSD自20世纪90年代成为流行病以来一直在东非蔓延，抗CBSD木薯克隆的发展缓慢，并且该疾病传播到西非的风险很高。我们建议使用工程技术的进步，我们的多光谱成像仪（MSI），快速确定坦桑尼亚田间植物的感染状况。它观察具有许多不同光谱的植物叶片，然后通过在木薯叶片扫描上训练的机器学习模型进行解释。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。我们将通过实验研究CBSD和参数传输模型的传播，以评估在清洁种子繁殖系统中具有较大下游效应的最关键领域中使用不同检测方法的胭脂效果。最后，使用这些参数化模型，我们将研究宿主易感性和环境因素如何驱动病媒丰度影响疾病压力，以及农民和农业代理商的模型干预措施，以最大限度地减少CBSD的区域传播和长期疾病压力。我们的研究将是第一个跟踪CBSD在该领域的传播，第一个准确地模拟这种紧急和破坏性木薯病原体的植物间传播，以及第一个将不同检测限的方法整合到具有显著营养繁殖的植物病理系统中。我们将创建用于木薯的精确工具，以及将我们的技术和模型用于其他具有经济意义的植物繁殖作物（如马铃薯、甘薯、芋头和山药）的病理系统的框架。更广泛的影响这项研究将对生活在受CBSD影响地区的人们的粮食安全产生重大影响，并减少进一步蔓延到撒哈拉以南非洲其他地区的可能性。木薯是八亿人的主要作物。我们将与坦桑尼亚木薯研究人员合作，与坦桑尼亚清洁种子计划建立联系，以确保将我们的工作结论转化为真实的应用的可能性很高。我们将在非洲的区域会议上，通过可访问的录音谈话，在科学会议上发表演讲，分享我们的成果。我们将积极从传统上被排除在外的群体中招募研究人员，并将培养至少四名博士后，一名博士生，一名研究助理和至少八名本科生。所有受训人员将接触一个研究非洲农业关键问题的多国研究小组。

Assuring food security for 8 billion people is one of the most pressing challenges of the 21st century. Insurance crops like cassava, which can withstand droughts and grow in nutrient-poor soil, are projected to play a key role in these efforts. However, cassava production in East Africa it is limited by RNA viruses that cause cassava brown streak disease (CBSD). CBSD causes subtle to no symptoms on stems and leaves while destroying the root tissue, which means farmers may be unaware their field is infected until they have a failed harvest. Distressingly, the visible symptoms are so slight that cuttings provided by 'clean seed' programs may be infected. While molecular diagnostics like PCR can determine if plants are infected, they are economically inviable for testing many plants in a field. Since there are no cures for CBSD, farmers can only rogue infected plants (remove them from the field before they can spread infection), but models of rouging based on visible symptoms show the method to be ineffective. Because it is difficult to observe infection prior to harvest, we do not know how quickly CBSD spreads in fields due to transient association with whitefly vectors, which has harmed modeling efforts. CBSD has been spreading in East Africa since it became epidemic in the 1990s, the development of CBSD-resistant cassava clones has been slow, and the disease is at high risk for spread to West Africa.We propose to use an engineering advancement, our multispectral imager (MSI), to rapidly determine the infection status of plants in the field in Tanzania. It observes the leaves of plants with many different light spectra, which 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. We will experimentally study the spread of CBSD and parameterize transmission models to assess the efficacy of rouging with different detection methods in the most critical fields with large downstream effects within the clean seed propagation system. Finally, using these parameterized models we will look at how host susceptibility and environmental factors driving vector abundance affect disease pressure, and model interventions by farmers and agricultural agents to minimize regional spread and standing disease pressure from CBSD. Intellectual MeritOur study will be the first to track CBSD spread in the field, the first to accurately model the plant-to-plant spread of this emergent and damaging cassava pathogen, and the first to integrate methods with different limits of detection into a plant pathosystem with significant vegetative propagation. We will create both accurate tools for use in cassava and frameworks for employing our technology and models for other economically significant pathosystems in vegetatively propagated crops, such as potato, sweet potato, taro and yam.Broader ImpactsThis research will significantly impact the food security of people living in areas affected by CBSD and reduce the likelihood of further spread into other regions of Sub-Saharan Africa, where cassava is a staple crop for 800 million people. We will partner with Tanzanian cassava researchers with connections to the Tanzanian clean seed program to assure a high likelihood of translation of the conclusions of our work to real applications. We will share our results through accessible recorded talks, at regional meetings in Africa, presentations at scientific conferences. We will actively recruit researchers from traditionally excluded groups and will train at least four postdocs, a PhD student, a research associate, and at least eight undergraduate students. All trainees will be exposed to a multinational research team working on a critical problem of African agriculture.