Developing AI to bridge lab and field plant research

开发人工智能以连接实验室和野外植物研究

• 批准号: BB/Y513969/1
• 负责人: Ji Zhou
• 金额: $ 32.31万
• 依托单位: National Institute of Agricultural Botany
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513969%2F1
• 关键词: Developing; AI; bridge; lab; field

The imminent challenges of climate change, growing food demand, and fertiliser shortage have brought enormous threats to global food security. As one of the most consumed grains in the world, wheat (Triticum aestivum L.) and its sustainable production are paramount to ensure food supply. The early parts of wheat developmental phase, i.e. germination (growth stage, GS 00-09) and seedling development (GS 10-19), are critical as a poor-quality establishment in the field can translate into: (1) a reduced plant density and thus a lower yield production, or (2) decreased competitiveness of crops against weeds and the development of diseases. In fact, better seed quality and vigour often lead to improved crop performance and health, ensuring crop sustainability under complex field conditions. Due to the importance of germination and seedling, the foundation phase in wheat underpins modern breeding, cultivation, agronomy, and even smart agricultural activities. Still, some lab-based research discoveries in seed vigour could vanish when they were moved to the field, which might be caused by genetics, environmental factors, agronomic management, and other in-field matters. AI-powered solutions could help the selection of the most predictive features from lab-based seed quality and vigour characteristics through a range of supervised or unsupervised algorithms, followed by the connection between lab-based features with in-field seedling performance. The above presents opportunities that AI could help bridge the gap between lab-based and field-based plant research: To identify spectral signatures (i.e. reflectance measured using single or multiple wavelengths to signify internal components of seeds) from multispectral seed imaging to assess seed quality. To establish AI-powered tracking methods to quantify radicle emergence and seedling establishment to quantify and classify seed vigour. To build a feature selection approach to select the most relevant features identified through lab-based experiments to predict and verify seedling development under field conditions. Both NIAB UK (led by Prof Ji Zhou) and Université d'Angers (University of Angers France, same below; led by Prof David Rousseau) have been developing AI solutions in areas such as multi-spectral seed imaging to assess seed quality, seed germination tests for seed vigour assessment, and drone-based phenotyping to study crop early establishment. This "Partner with international researchers on AI for Bioscience" call will provide a unique opportunity for both sides to work together, learning from counterpart's AI solutions, jointly optimising the existing toolkits, and more importantly, seeking and developing AI-powered solutions to connect lab-based seed quality and vigour assessment with in-field seedling establishment using wheat as a model plant. We trust the proposed project will be a valuable case study that demonstrates how to combine AI and domain knowledge to bridge the gap between laboratory and field-based plant research.

气候变化、不断增长的粮食需求和化肥短缺等迫在眉睫的挑战给全球粮食安全带来了巨大威胁，小麦作为世界上消耗量最大的谷物之一，其可持续生产对于保障粮食至关重要。小麦发育阶段的早期阶段，即发芽（生长阶段，GS 00-09）和幼苗发育（GS 10-19）至关重要，因为田间质量差可能会导致： (1) 植物密度降低，从而降低产量，或 (2) 作物对抗杂草和疾病的竞争力下降。事实上，更好的种子质量和活力往往会改善作物性能和健康，确保作物的可持续性。由于发芽和幼苗的重要性，小麦的基础阶段支撑着现代育种、栽培、农学甚至智能农业活动，但一些基于实验室的种子活力研究发现可能会在转移后消失。到田间的差异可能是由遗传、环境因素、农艺管理和其他田间问题引起的，可以帮助通过一系列监督或无监督的方式从基于实验室的种子质量和活力特征中选择大多数特征。算法，然后是基于实验室的特征与田间育苗性能之间的联系。 上述内容提供了人工智能可以帮助弥合基于实验室和基于田间的植物研究之间差距的机会：识别光谱特征（即使用单个测量的反射率）。或多个建立人工智能驱动的跟踪方法来量化胚根的出现和幼苗的建立，以量化和分类种子活力。通过基于实验室的实验来预测和验证田间条件下的幼苗发育，NIAB UK（由 Ji Zhou 教授领导）和 Université d'Angers（法国昂热大学，下同；由 David Rousseau 教授领导）都确定了这一点。一直在开发用于评估种子质量的多光谱种子成像、用于评估种子活力的种子发芽测试以及用于研究作物早期生长的基于无人机的表型等领域的人工智能解决方案。将为双方提供一个独特的合作机会，学习对方的人工智能解决方案，共同优化现有工具包，更重要的是，寻求和开发人工智能驱动的解决方案，将实验室种子质量和活力评估与田间联系起来我们相信，所提出的项目将是一个有价值的案例研究，展示如何将人工智能和领域知识结合起来，弥合实验室和实地植物研究之间的差距。