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

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: BB/X018792/1
• 负责人: Bruce Grieve
• 金额: $ 93.6万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX018792%2F1
• 关键词: Presymptomatic; detection; multispectral; imaging; quantify

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.

确保80亿人的粮食安全是21世纪最紧迫的挑战之一。预计木薯等保险作物将在这些努力中发挥关键作用，因为木薯可以抵御干旱，在营养贫乏的土壤中生长。然而，东非的木薯生产受到导致木薯褐条病（CBSD）的RNA病毒的限制。在破坏根组织的同时，CBSD会在茎和叶上引起轻微甚至没有症状，这意味着农民可能直到收成不佳才意识到他们的田地被感染了。令人苦恼的是，可见的症状非常轻微，“清洁种子”项目提供的插枝可能会被感染。虽然像PCR这样的分子诊断方法可以确定植物是否被感染，但从经济上讲，它们不适合在一块地里检测许多植物。由于没有治疗CBSD的方法，农民只能让受感染的植物流浪（在它们传播感染之前把它们从田地里移走），但是基于可见症状的种植模型表明，这种方法是无效的。由于很难在收获前观察感染情况，我们不知道CBSD在田间传播的速度有多快，因为它与白蝇载体的短暂联系损害了建模工作。自20世纪90年代CBSD在东非流行以来，该疾病一直在东非蔓延，对CBSD具有抗性的木薯无性系的发展缓慢，并且该疾病具有向西非传播的高风险。我们建议使用一项工程进步，即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况。它用许多不同的光谱观察植物的叶子，然后通过对木薯叶子扫描进行训练的机器学习模型来解释这些光谱。在实验室条件下，MSI在感染后28天检测CBSD感染的准确率为95%，此时植物没有明显的症状。我们将通过实验研究CBSD的传播，并对传播模型进行参数化，以评估不同检测方法在清洁种子传播系统中下游影响较大的最关键领域的效果。最后，使用这些参数化模型，我们将研究宿主易感性和驱动媒介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商的干预措施，以最大限度地减少CBSD的区域传播和持续疾病压力。我们的研究将是第一个追踪CBSD在田间传播的研究，第一个准确模拟这种紧急和有害的木薯病原体在植物间的传播，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和框架，以便将我们的技术和模型应用于马铃薯、甘薯、芋头和山药等无性繁殖作物的其他经济上重要的病害系统。更广泛的影响这项研究将显著影响生活在受CBSD影响地区的人们的粮食安全，并减少进一步蔓延到撒哈拉以南非洲其他地区的可能性，木薯是该地区8亿人的主要作物。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极有可能转化为实际应用。我们将通过可获取的录音谈话、在非洲的区域会议、在科学会议上的发言来分享我们的成果。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。所有受训者都将接触到一个研究非洲农业关键问题的多国研究小组。