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Assessing robustness and transferability of image‐based semantic segmentation models for major field crops

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Published in:Plant Phenome Journal
Format: Online Article RSS Article
Published: 2026
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container_title Plant Phenome Journal
description
discipline_display Agronomy and Crop Science
discipline_facet Agronomy and Crop Science
format Online Article
RSS Article
genre Journal Article
id rss_article:84566
institution FRELIP
journal_source_facet Plant Phenome Journal
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
Agronomy and Crop Science
General
Agronomy and Crop Science
sub_discipline_display General
sub_discipline_facet General
subject_display Agronomy and Crop Science
General
Agronomy and Crop Science
Agronomy and Crop Science
General
Agronomy and Crop Science
subject_facet Agronomy and Crop Science
General
Agronomy and Crop Science
title Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_auth Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_full Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_fullStr Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_full_unstemmed Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_short Assessing robustness and transferability of image‐based semantic segmentation models for major field crops
title_sort assessing robustness and transferability of image‐based semantic segmentation models for major field crops
topic Agronomy and Crop Science
General
Agronomy and Crop Science
url https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.70087?af=R