Developing AI to bridge lab and field 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(Triticum aestivum L.)是世界上消费量最大的谷物之一。其可持续生产对确保粮食供应至关重要。小麦发育的早期阶段，即萌发(生长期，GS 00-09)和幼苗发育(GS 10-19)是至关重要的，因为田间低质量的设施可能会转化为：(1)种植密度降低，从而降低产量，或(2)作物对杂草和病害的竞争力降低。事实上，更好的种子质量和活力往往会改善作物的表现和健康，确保作物在复杂田间条件下的可持续性。由于发芽和幼苗的重要性，小麦的基础阶段是现代育种、栽培、农艺甚至智能农业活动的基础。尽管如此，一些基于实验室的种子活力研究发现，当它们转移到田间时，可能会消失，这可能是由于遗传、环境因素、农艺管理和其他田间问题造成的。人工智能支持的解决方案可以通过一系列监督或非监督算法，帮助从基于实验室的种子质量和活力特征中选择最具预测性的特征，然后将基于实验室的特征与田间苗木表现联系起来。以上内容提供了人工智能可以帮助弥合实验室和田间植物研究之间的差距的机会：从多光谱种子成像中识别光谱特征(即使用单个或多个波长测量的反射率来表示种子的内部成分)，以评估种子质量。建立人工智能支持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。建立一种特征选择方法来选择通过实验室实验确定的最相关的特征，以预测和验证田间条件下的苗木发育。NIAB UK(由纪周教授领导)和Universityéd‘Angers(法国昂格斯大学，下同；David Rousseau教授领导)一直在开发用于评估种子质量的多光谱种子成像、用于种子活力评估的种子萌发测试以及用于研究作物早期建立的无人机表型等领域的人工智能解决方案。这一“与国际生物科学人工智能研究人员合作”的呼吁将为双方提供一个独特的合作机会，学习同行的人工智能解决方案，共同优化现有的工具包，更重要的是，寻找和开发人工智能支持的解决方案，将基于实验室的种子质量和活力评估与以小麦为模式植物的田间苗木建立联系起来。我们相信，拟议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，以弥合实验室和田间植物研究之间的差距。

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.