Poster
Siyuan Wei
University of Cambridge
CAMBRIDGE, England, United Kingdom
Olaf Kranse
University of Cambridge
Cambridge, England, United Kingdom
Jie Zhou
Nanjing Agricultural University
Nanjing, Jiangsu, China (People's Republic)
Ji Zhou
Head of Plant Phenomics Research Center
Nanjing Agricultural University
Nanjing, Jiangsu, China (People's Republic)
Sebastian Eves-Van Den Akker, PhD
The Crop Science Centre, Department of Plant Sciences, University of Cambridge
Cambridge, England, United Kingdom
Plant-parasitic nematodes pose a major threat to global food security, yet identifying resistance sources is hindered by phenotyping limitations. To address this, a novel high-throughput, low-cost phenotyping platform that integrates 3D-printed hardware with deep-learning-based trait recognition was developed. Using this platform, we screened the Arabidopsis Multiparent Advanced Generation InterCross (MAGIC) population for susceptibility to the beet cyst nematode Heterodera schachtii. Over 90 days, we phenotyped 527 recombinant inbred lines (20 replicates each; ~10,000 plants), generating ~400,000 phenotyping events and analysing tens of millions of nematodes. This holistic, dynamic approach captures the entire plant life cycle, setting a new standard in plant pathology. From this dataset, we identified novel plant loci linked to nematode susceptibility and resistance via genome-wide association studies and defined new fundamental features of nematode parasitism with high confidence. Our work demonstrates the transformative potential of AI in advancing the understanding and control of plant-parasitic nematodes, offering critical insights for resistance breeding and global food security.