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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F23%3A43924191" target="_blank" >RIV/62156489:43410/23:43924191 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1186/s13007-023-01101-2" target="_blank" >https://doi.org/10.1186/s13007-023-01101-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13007-023-01101-2" target="_blank" >10.1186/s13007-023-01101-2</a>

Result language

angličtina

Original language name

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

Original language description

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 &gt; 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40102 - Forestry

Project

—

Continuities

O - Projekt operacniho programu

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plant Methods

ISSN

1746-4811

e-ISSN

1746-4811

Volume of the periodical

19

Issue of the periodical within the volume

6 November

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

122

UT code for WoS article

001099034900001

EID of the result in the Scopus database

2-s2.0-85175806863