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

• URL: http://arxiv.org/abs/2301.10351v3
• Date: Thu, 18 May 2023 12:10:54 GMT
• Title: Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
• Authors: John Lagergren, Mirko Pavicic, Hari B. Chhetri, Larry M. York, P. Doug Hyatt, David Kainer, Erica M. Rutter, Kevin Flores, Jack Bailey-Bale, Marie Klein, Gail Taylor, Daniel Jacobson, Jared Streich
• Abstract summary: This 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.
• Score: 1.9089478605920305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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