Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Pavel Baykalov, Bart Bussmann, Richard Nair, Abraham George Smith, Gernot Bodner, Ofer Hadar, Naftali Lazarovitch, Boris Rewald

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset. Results: The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives. Conclusions: Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts—limiting the need for model retraining.

Original languageEnglish
Article number122
JournalPlant Methods
Volume19
Issue number1
DOIs
StatePublished - 1 Dec 2023

Keywords

  • Automatic image segmentation
  • Data augmentation
  • Deep learning
  • False positives
  • Fine roots
  • Image processing
  • Minirhizotron
  • Neural networks
  • Root segmentation
  • U-Net

ASJC Scopus subject areas

  • Genetics
  • Biotechnology
  • Plant Science

Fingerprint

Dive into the research topics of 'Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models'. Together they form a unique fingerprint.

Cite this