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WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging
Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and develo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485698/ https://www.ncbi.nlm.nih.gov/pubmed/37692423 http://dx.doi.org/10.3389/fpls.2023.1226190 |
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author | Batin, M. A. Islam, Muhaiminul Hasan, Md Mehedi Azad, AKM Alyami, Salem A. Hossain, Md Azam Miklavcic, Stanley J. |
author_facet | Batin, M. A. Islam, Muhaiminul Hasan, Md Mehedi Azad, AKM Alyami, Salem A. Hossain, Md Azam Miklavcic, Stanley J. |
author_sort | Batin, M. A. |
collection | PubMed |
description | Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model’s hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants. |
format | Online Article Text |
id | pubmed-10485698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104856982023-09-09 WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging Batin, M. A. Islam, Muhaiminul Hasan, Md Mehedi Azad, AKM Alyami, Salem A. Hossain, Md Azam Miklavcic, Stanley J. Front Plant Sci Plant Science Phenotyping is used in plant breeding to identify genotypes with desirable characteristics, such as drought tolerance, disease resistance, and high-yield potentials. It may also be used to evaluate the effect of environmental circumstances, such as drought, heat, and salt, on plant growth and development. Wheat spike density measure is one of the most important agronomic factors relating to wheat phenotyping. Nonetheless, due to the diversity of wheat field environments, fast and accurate identification for counting wheat spikes remains one of the challenges. This study proposes a meticulously curated and annotated dataset, named as SPIKE-segm, taken from the publicly accessible SPIKE dataset, and an optimal instance segmentation approach named as WheatSpikeNet for segmenting and counting wheat spikes from field imagery. The proposed method is based on the well-known Cascade Mask RCNN architecture with model enhancements and hyperparameter tuning to provide state-of-the-art detection and segmentation performance. A comprehensive ablation analysis incorporating many architectural components of the model was performed to determine the most efficient version. In addition, the model’s hyperparameters were fine-tuned by conducting several empirical tests. ResNet50 with Deformable Convolution Network (DCN) as the backbone architecture for feature extraction, Generic RoI Extractor (GRoIE) for RoI pooling, and Side Aware Boundary Localization (SABL) for wheat spike localization comprises the final instance segmentation model. With bbox and mask mean average precision (mAP) scores of 0.9303 and 0.9416, respectively, on the test set, the proposed model achieved superior performance on the challenging SPIKE datasets. Furthermore, in comparison with other existing state-of-the-art methods, the proposed model achieved up to a 0.41% improvement of mAP in spike detection and a significant improvement of 3.46% of mAP in the segmentation tasks that will lead us to an appropriate yield estimation from wheat plants. Frontiers Media S.A. 2023-08-25 /pmc/articles/PMC10485698/ /pubmed/37692423 http://dx.doi.org/10.3389/fpls.2023.1226190 Text en Copyright © 2023 Batin, Islam, Hasan, Azad, Alyami, Hossain and Miklavcic https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Batin, M. A. Islam, Muhaiminul Hasan, Md Mehedi Azad, AKM Alyami, Salem A. Hossain, Md Azam Miklavcic, Stanley J. WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title | WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title_full | WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title_fullStr | WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title_full_unstemmed | WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title_short | WheatSpikeNet: an improved wheat spike segmentation model for accurate estimation from field imaging |
title_sort | wheatspikenet: an improved wheat spike segmentation model for accurate estimation from field imaging |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10485698/ https://www.ncbi.nlm.nih.gov/pubmed/37692423 http://dx.doi.org/10.3389/fpls.2023.1226190 |
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