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 (CNNs) to segment the leaf body and visible venation of 2,906 P. trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (i) does not require experimental or image pre-processing, (ii) uses the raw RGB images at full resolution, and (iii) requires very few samples for training (e.g., just eight images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

Climate adaptation inP. trichocarpa: key adaptive loci identified for stomata and leaf traits

“…Subsequently, we captured images of the contact surface using a Canon EOS 500D camera. Similar to stomatal detection, we used a convolutional neural network implemented in PyTorch to distinguish water droplets following the region growing methodology (Lagergren et al, 2023). Using a dataset of 96 images for training and 23 for testing, we achieved a segmentation accuracy (Sørensen-Dice coefficient) of 0.917.…”