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Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning

Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study,...

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Autores principales: Jia, Zhicheng, Sun, Ming, Ou, Chengming, Sun, Shoujiang, Mao, Chunli, Hong, Liu, Wang, Juan, Li, Manli, Jia, Shangang, Mao, Peisheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572871/
https://www.ncbi.nlm.nih.gov/pubmed/36236620
http://dx.doi.org/10.3390/s22197521
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author Jia, Zhicheng
Sun, Ming
Ou, Chengming
Sun, Shoujiang
Mao, Chunli
Hong, Liu
Wang, Juan
Li, Manli
Jia, Shangang
Mao, Peisheng
author_facet Jia, Zhicheng
Sun, Ming
Ou, Chengming
Sun, Shoujiang
Mao, Chunli
Hong, Liu
Wang, Juan
Li, Manli
Jia, Shangang
Mao, Peisheng
author_sort Jia, Zhicheng
collection PubMed
description Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor.
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spelling pubmed-95728712022-10-17 Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning Jia, Zhicheng Sun, Ming Ou, Chengming Sun, Shoujiang Mao, Chunli Hong, Liu Wang, Juan Li, Manli Jia, Shangang Mao, Peisheng Sensors (Basel) Article Multispectral imaging (MSI) has become a new fast and non-destructive detection method in seed identification. Previous research has usually focused on single models in MSI data analysis, which always employed all features and increased the risk to efficiency and that of system cost. In this study, we developed a stacking ensemble learning (SEL) model for successfully identifying a single seed of sickle alfalfa (Medicago falcata), hybrid alfalfa (M. varia), and alfalfa (M. sativa). SEL adopted a three-layer structure, i.e., level 0 with principal component analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as models of dimensionality reduction and feature extraction (DRFE); level 1 with support vector machine (SVM), multiple logistic regression (MLR), generalized linear models with elastic net regularization (GLMNET), and eXtreme Gradient Boosting (XGBoost) as basic learners; and level 3 with XGBoost as meta-learner. We confirmed that the values of overall accuracy, kappa, precision, sensitivity, specificity, and sensitivity in the SEL model were all significantly higher than those in basic models alone, based on both spectral features and a combination of morphological and spectral features. Furthermore, we also developed a feature filtering process and successfully selected 5 optimal features out of 33 ones, which corresponded to the contents of chlorophyll, anthocyanin, fat, and moisture in seeds. Our SEL model in MSI data analysis provided a new way for seed identification, and the feature filter process potentially could be used widely for development of a low-cost and narrow-channel sensor. MDPI 2022-10-04 /pmc/articles/PMC9572871/ /pubmed/36236620 http://dx.doi.org/10.3390/s22197521 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jia, Zhicheng
Sun, Ming
Ou, Chengming
Sun, Shoujiang
Mao, Chunli
Hong, Liu
Wang, Juan
Li, Manli
Jia, Shangang
Mao, Peisheng
Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title_full Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title_fullStr Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title_full_unstemmed Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title_short Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning
title_sort single seed identification in three medicago species via multispectral imaging combined with stacking ensemble learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572871/
https://www.ncbi.nlm.nih.gov/pubmed/36236620
http://dx.doi.org/10.3390/s22197521
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