Detection and diagnosis of seed-borne diseases utilising machine learning enhanced gas plasma integrated multispectral imaging (DeTecSeeD)

以农作物种子为媒介的致病病原体、真菌和细菌的迅速出现和全球传播(种子传播的疾病)威胁着农业的可持续性和粮食安全。食源性疾病是一种无处不在的威胁，往往与食用园艺产品等新鲜食品有关。英国食品标准局(FSA)进行的一项名为《2018年英国食源性疾病的负担》的研究发现，据估计，英国每年有240万例食源性疾病病例，食源性疾病造成的总负担约为90亿英镑。由病原体污染引起的食源性疾病可能是灾难性的，并可能因食源性疾病、慢性残疾和死亡而给消费者带来“痛苦、悲伤和痛苦”。这方面的一个例子是2011年爆发的用于芽苗栽培的进口园艺种子引起的肠出血性大肠杆菌感染(&gt；50人死亡)。蔬菜作物经常被种子传播的真菌病原体侵染。在这种情况下，真菌或细菌已经存在于种子内部或种子外表面，这会降低种子质量，并可能在作物形成早期导致种子腐烂和幼苗倒伏。用杀菌剂处理种子用于控制蔬菜作物种子传播的真菌病原体，但自2019年以来，欧盟法规禁止使用最多的杀菌剂福美双。2017年，联合国粮农组织发布了新的联合国指导方针和植物检疫措施，以使对世界人口至关重要的植物和种子国际贸易更加安全。种子传播的疾病特别令人担忧，因为园艺和农业作物种子用于育苗(例如生菜)或直接播种(例如卷心菜)，因此它们携带的任何害虫都可能在作物生产期间滋生和传播。作物种子健康的一个关键问题是，用常规方法(如病原菌分离和生长分析或DNA条形码分子鉴定)检测和诊断种子传播的病原菌是费力和耗时的过程，需要专业知识。因此，种业、官方种子检测站和边境商业作物种子的国际贸易迫切需要快速、敏感、可靠和高效的种子病原体检测和诊断技术平台。DeTecSeeD(种子传播疾病检测技术)TRDF项目联盟将探索开发一种检测和诊断种子传播疾病的新技术。伦敦皇家霍洛威大学(RHUL)种子科学和技术组负责人Gerhard Leubner教授、RHUL种子生物物理学专家Tina Steinbrecher博士和拉夫堡大学(LU)沃尔夫森机械、电气和制造工程学院的Felipe Iza博士成立了一个跨学科团队，探索机器学习增强型气体等离子体集成多光谱成像是否可能为种子传播疾病提供一种新的检测和诊断方法。这种方法是跨学科的，预期对研究界的影响是广泛的。成功的概念验证将使未来的工作能够转化结果，以构建具有广泛适用性的易于使用的设备。

The rapid emergence and global spreading of pathogens, fungi and bacteria causing diseases, with crop seeds as vehicles (seed-borne diseases) are a threat to agricultural sustainability and food security. Food-borne disease is an ever-present threat and often associated with the consumption of fresh food such as horticultural products. A research study conducted by the UK's Food Standards Agency (FSA) entitled 'The Burden of Food-borne Disease in the UK 2018' identified an estimate of 2.4 million foodborne disease cases per year, the total burden from food-borne illness is ~£9bn in the UK. Food-borne diseases due to pathogen contamination can be disastrous and can cause 'pain, grief and suffering' for the consumers due to food-borne related illness, chronic disability and fatalities. An example for this is the 2011 outbreak of thousands of infections (>50 deaths) with enterohemorrhagic Escherichia coli (EHEC) caused by imported horticultural seeds used for sprout cultivation. Vegetable crops are frequently infected with seed-borne fungal pathogens. In such cases the fungi or bacteria are already present inside the seed or on the outer seed surface, which reduces seed quality and can cause seed rot and seedling damping-off during early crop establishment. Seed treatment with fungicides used to control seed-borne fungal pathogens of vegetable crops, but the most used fungicide thiram is banned in the EU since 2019 by regulations. In 2017 the FAO (United Nation Food and Agriculture Organization) issued new UN guidelines and phytosanitary measures to make international trade with plants and seeds - vital to feed the world's population - safer. Seed-borne diseases are of particular concern since horticultural and agricultural crop seeds are used for seedling raising (e.g. lettuce) or direct sowing (e.g. cabbage) and thereby any pests they carry could establish themselves and spread during crop production. A critical issue with regard to crop seed health is that the detection and diagnosis of seed-borne pathogens by conventional methods (e.g. pathogen isolation and growth analysis or molecular identification by DNA barcoding) are laborious and time-consuming processes which require expert knowledge. Novel detection and diagnosis technology platforms for seed-borne pathogens which are rapid, sensitive, reliable and efficient is therefore urgently needed in seed industry, at official seed testing stations, and for the international trade with commercial crop seed at borders. The DeTecSeeD (Detection Technology for Seed-borne Diseases) TRDF project consortium will explore the development of a new technology for the detection and diagnosis of seed-borne diseases. Prof Gerhard Leubner, Head of Royal Holloway University of London's (RHUL) Seed Science and Technology Group, Dr Tina Steinbrecher, RHUL's expert in seed biophysics, and Dr Felipe Iza from Loughborough University (LU) Wolfson School of Mechanical, Electrical and Manufacturing Engineering, have established an interdisciplinary team to explore if machine learning enhanced gas plasma integrated multispectral imaging may provide a novel detection and diagnosis of seed-borne diseases. The approach is interdisciplinary and the expected impact on the research community wide-reaching. Successful proof-of-concept will enable future work into translating the findings for building easy-to-use devices with wide applicability.