Developing AI to bridge lab and field plant research

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.