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。像木薯这样的保险作物可以抵御干旱，生长在养分贫瘠的土壤中，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在破坏根组织的同时，会在茎和叶上造成轻微甚至没有症状，这意味着农民可能不知道他们的田地受到了感染，直到他们歉收。令人痛心的是，可见的症状非常轻微，以至于“清洁种子”计划提供的插条可能会被感染。虽然像PCR这样的分子诊断技术可以确定植物是否受到感染，但在一块田里测试许多植物在经济上是不可行的。由于CBSD没有治愈方法，农民只能对受感染的植物进行无赖处理(在它们传播感染之前将它们从田里移走)，但基于可见症状的胭脂模型表明这种方法无效。由于在收获前很难观察到感染情况，我们不知道CBSD在田间传播的速度有多快，这是由于与烟粉虱媒介的瞬时关联，这损害了建模工作。CBSD自20世纪90年代流行以来一直在东非传播，抗CBSD的木薯无性系发展缓慢，该疾病传播到西非的风险很高。我们建议使用我们的多光谱成像仪(MSI)来快速确定坦桑尼亚田间植物的感染状态。它观察具有许多不同光谱的植物叶片，然后通过机器学习模型对木薯叶片扫描进行解释。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%。我们将对CBSD的传播进行实验研究，并将传播模型参数化，以评估在清洁种子繁殖系统中具有较大下游影响的最关键领域使用不同检测方法进行转印的效果。最后，使用这些参数化模型，我们将研究宿主易感性和驱动病媒丰度的环境因素如何影响疾病压力，并模拟农民和农业代理人的干预措施，以最大限度地减少来自CBSD的区域传播和长期疾病压力。智慧价值我们的研究将是第一个跟踪CBSD在田间传播的研究，第一个准确模拟这种紧急和破坏性木薯病原菌在植物之间传播的研究，以及第一个将不同检测限度的方法集成到具有显著营养繁殖的植物病理系统中的研究。我们将创建用于木薯的准确工具，以及将我们的技术和模型应用于其他具有重要经济意义的病理系统的框架，用于无性繁殖作物，如马铃薯、红薯、芋和山药。广泛影响这项研究将显著影响受CBSD影响地区人民的粮食安全，并降低进一步传播到撒哈拉以南非洲其他地区的可能性，那里的木薯是8亿人的主食作物。我们将与与坦桑尼亚清洁种子计划有联系的坦桑尼亚木薯研究人员合作，以确保我们的工作结论转化为实际应用的可能性很高。我们将通过可访问的录音演讲、在非洲的区域会议上、在科学会议上的陈述来分享我们的成果。我们将积极从传统上被排斥的群体中招募研究人员，并将培养至少4名博士后、1名博士生、1名研究助理和至少8名本科生。所有受训人员都将接触到一个致力于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.