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Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or...

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Autores principales: Rahaman, Md. Matiur, Ahsan, Md. Asif, Chen, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925301/
https://www.ncbi.nlm.nih.gov/pubmed/31862925
http://dx.doi.org/10.1038/s41598-019-55609-6
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author Rahaman, Md. Matiur
Ahsan, Md. Asif
Chen, Ming
author_facet Rahaman, Md. Matiur
Ahsan, Md. Asif
Chen, Ming
author_sort Rahaman, Md. Matiur
collection PubMed
description Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
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spelling pubmed-69253012019-12-24 Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification Rahaman, Md. Matiur Ahsan, Md. Asif Chen, Ming Sci Rep Article Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy. Nature Publishing Group UK 2019-12-20 /pmc/articles/PMC6925301/ /pubmed/31862925 http://dx.doi.org/10.1038/s41598-019-55609-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Rahaman, Md. Matiur
Ahsan, Md. Asif
Chen, Ming
Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title_full Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title_fullStr Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title_full_unstemmed Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title_short Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification
title_sort data-mining techniques for image-based plant phenotypic traits identification and classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925301/
https://www.ncbi.nlm.nih.gov/pubmed/31862925
http://dx.doi.org/10.1038/s41598-019-55609-6
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