Scalable-Infield Phenotyping for DNA Measurements of Performance Related Traits to Study Gene Introgression in Bread Wheat

Automated in-field phenotyping can provide continuous and precise measures of adaptation and performance traits that are key to today’s breeding pipelines and agricultural practices. In my talk, I will introduce our integrated field phenotyping systems at John Innes Centre (JIC) and Earlham Institute (EI), including UAV (unmanned aerial vehicles), 3D scanning crop phenotyping platform (Phenospex), cost-effective CropQuant workstations and other novel hardware/software solutions that facilitate high-resolution and high-frequency crop phenotyping. In particular, I will talk about how we are utilising phenotypic information to empower the assessment of genes controlling yield potential and environmental adaptation which are key to study gene introgression. I will introduce CropQuant, a cost-effective IoT (Internet of Things) powered platform that has incorporated networked sensors, single-board computers, in-field wireless communication and open high-throughput analysis algorithms to process field experimentation data. Besides trait analysis, we have established machine-learning based models to explore and predict the dynamics between genotype, phenotype and environment (GxPxE). A proof-of-principle example based on Near-isogenic lines (NILs) of wheat genotypes such as Ppd-1 (loss of function), Ppd-D1a (photoperiod insensitivity), Rht-D1b (semi dwarfing), stay green induced mutants, and Lr19 (hypersensitive response to the pathogen), and Paragon wild type will be also discussed in the talk.