Unlocking the potential of wheat grain heterogeneity using machine vision

利用机器视觉释放小麦籽粒异质性的潜力

• 批准号: BB/W006979/1
• 负责人: Ian Fisk
• 金额: $ 97.72万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW006979%2F1
• 关键词: Unlocking; potential; wheat; grain; heterogeneity

This project will unlock the potential of wheat grain heterogeneity. We will: 1) Develop a novel single seed phenotyping tool based on hyperspectral imaging technology (HSI) integrated with next generation machine learning 2) Explain the determinism of the variance of uniformity of single seed grain quality parameters and explore a broad range of both known, and novel and exotic wheat genotypes for previously undefinable unique single seed traits, this will allow breeders to target previously unavailable grain quality uniformity traits, as well as speed selection from segregating populations. 3) Deploy the single grain HSI technology as a novel molecular breeding tool by determining key genes controlling single grain quality uniformity traits and validating the candidate genes by developing lines with contrasting expressing of the novel genes which we will test in field experiments. 4) Demonstrate the application of the single seed phenotyping tool as a sorting technology at laboratory and pilot production scale for wheat. This will demonstrate the ultimate value of the approach by producing exemplar food products (bread, biscuit and malted wheat) with enhanced quality and health credentials and validating the findings through sensory and consumer insight testing.Ultimately this project offers the potential for breeders to significantly upgrade the UK wheat grain production, reduce the requirements to use imported wheat of millers, and enhance the nutritional quality and sensory quality traits of bread, biscuits and food products containing malted wheat for the consumer.The impact of this project will be very significant as sorting by hyperspectral classification for protein content would allow tighter segregation of the wheat supply chain into defined applications such as those that require lower protein (cakes, biscuits, pastry) from those that require higher protein with good protein quality and consistency and resulting good rheology (bread, pasta, high protein flour) and allow tighter adherence to supplier specifications in addition to reducing the need of imported wheat. At the highest capacities, a single sorting machine can process around 0.5 million tons per year, this indicates a very significant impact on the UK wheat industry with a relatively low-cost intervention, often in centralised milling sites. Furthermore, premium wheat with unique bread-making properties (e.g. elevated micronutrients, very high protein) and unique flavour potential through the malting process, will be sold with a price premium. If a further 20% of UK farmers growing bread-making wheat varieties were to achieve the grain protein market specification of 13% for the premium each year, it would be worth an extra £25 M per year to the UK agriculture sector.

该项目将释放小麦籽粒异质性的潜力。我们将：1）基于与下一代机器学习集成的高光谱成像技术（HSI）开发一种新型单种子表型工具2）解释单种子籽粒质量参数均匀性方差的决定性，并探索广泛的已知、新型和外来小麦基因型，以获得以前无法定义的独特单种子性状，这将使育种者能够瞄准以前无法获得的谷物品质均匀性性状，以及从分离群体中进行快速选择。3)通过确定控制单粒品质一致性性状的关键基因，并通过培育新基因表达的对照系来验证候选基因，将单粒HSI技术作为一种新的分子育种工具进行部署，我们将在田间试验中测试新基因的表达。4)展示单粒种子表型分析工具作为小麦实验室和中试生产规模分选技术的应用。这将通过生产示范食品来证明这种方法的最终价值（面包，饼干和麦芽小麦），提高质量和健康证书，并通过感官和消费者洞察力测试验证结果。最终，该项目为育种者提供了显着提升英国小麦产量的潜力，减少了对米莱尔使用进口小麦的要求，提高面包的营养品质和感官品质性状，该项目的影响将非常重大，因为通过高光谱分类对蛋白质含量进行分类将使小麦供应链更紧密地分离到定义的应用中，例如从需要高蛋白质、蛋白质质量和稠度好、流变性好的产品（面包、意大利面、高蛋白面粉）中分离出需要较低蛋白质的产品（蛋糕、饼干、糕点），并且除了减少对进口小麦的需求外，还能更严格地遵守供应商的规格。在最高能力下，一台分拣机每年可处理约50万吨小麦，这表明对英国小麦行业产生了非常重大的影响，而且通常在集中的碾磨场地进行相对低成本的干预。此外，具有独特面包制作特性（例如微量营养素含量高，蛋白质含量极高）和通过制麦工艺获得独特风味潜力的优质小麦将以溢价出售。如果另外20%的英国农民种植面包小麦品种，以达到每年13%的溢价谷物蛋白质市场规格，那么英国农业部门每年将额外增加2500万英镑。

项目成果

Challenges facing sustainable protein production: Opportunities for cereals.

• DOI: 10.1016/j.xplc.2023.100716
• 发表时间: 2023-11-13
• 期刊: PLANT COMMUNICATIONS
• 影响因子: 10.5
• 作者: Safdar, Luqman B.;Foulkes, M. John;Kleiner, Friedrich H.;Searle, Iain R.;Bhosale, Rahul A.;Fisk, Ian D.;Boden, Scott A.
• 通讯作者: Boden, Scott A.

Integration of hyperspectral imaging, non-targeted metabolomics and machine learning for vigour prediction of naturally and accelerated aged sweetcorn seeds

• DOI: 10.1016/j.foodcont.2023.109930
• 发表时间: 2023
• 期刊: Food Control
• 影响因子: 6
• 作者: Tingting Zhang;Long Lu;N. Yang;Ian D Fisk;Wensong Wei;Li Wang;Jing Li;Qun Sun;Rensen Zeng