Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa.

ABSTRACT: Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf

SUBMITTER: Lagergren J 

PROVIDER: S-EPMC10380552 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature