Cargando…

Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset

Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninva...

Descripción completa

Detalles Bibliográficos
Autores principales: Rößle, Dominik, Prey, Lukas, Ramgraber, Ludwig, Hanemann, Anja, Cremers, Daniel, Noack, Patrick Ole, Schön, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348660/
https://www.ncbi.nlm.nih.gov/pubmed/37456082
http://dx.doi.org/10.34133/plantphenomics.0068
_version_ 1785073713653219328
author Rößle, Dominik
Prey, Lukas
Ramgraber, Ludwig
Hanemann, Anja
Cremers, Daniel
Noack, Patrick Ole
Schön, Torsten
author_facet Rößle, Dominik
Prey, Lukas
Ramgraber, Ludwig
Hanemann, Anja
Cremers, Daniel
Noack, Patrick Ole
Schön, Torsten
author_sort Rößle, Dominik
collection PubMed
description Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen’s kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the [Formula: see text] score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.
format Online
Article
Text
id pubmed-10348660
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher AAAS
record_format MEDLINE/PubMed
spelling pubmed-103486602023-07-15 Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset Rößle, Dominik Prey, Lukas Ramgraber, Ludwig Hanemann, Anja Cremers, Daniel Noack, Patrick Ole Schön, Torsten Plant Phenomics Research Article Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen’s kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the [Formula: see text] score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification. AAAS 2023-07-14 /pmc/articles/PMC10348660/ /pubmed/37456082 http://dx.doi.org/10.34133/plantphenomics.0068 Text en Copyright © 2023 Dominik Rößle et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Rößle, Dominik
Prey, Lukas
Ramgraber, Ludwig
Hanemann, Anja
Cremers, Daniel
Noack, Patrick Ole
Schön, Torsten
Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title_full Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title_fullStr Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title_full_unstemmed Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title_short Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset
title_sort efficient noninvasive fhb estimation using rgb images from a novel multiyear, multirater dataset
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348660/
https://www.ncbi.nlm.nih.gov/pubmed/37456082
http://dx.doi.org/10.34133/plantphenomics.0068
work_keys_str_mv AT roßledominik efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT preylukas efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT ramgraberludwig efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT hanemannanja efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT cremersdaniel efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT noackpatrickole efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset
AT schontorsten efficientnoninvasivefhbestimationusingrgbimagesfromanovelmultiyearmultiraterdataset