MRI-Seed-Wizard: Combining Deep Learning Algorithms with Magnetic Resonance Imaging Enables Advanced Seed Phenotyping.

Abstract

Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programs. There is need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduced a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for its use in the new domain - plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analyzing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping was demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality - MRI, opens up new perspectives in seed phenotyping and crop improvement.