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Volume 22
Issue 19
10.3390/s22197521
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Article

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

by
Zhicheng Jia
,
Ming Sun
,
Chengming Ou
,
Shoujiang Sun
,
Chunli Mao
,
Liu Hong
,
Juan Wang
,
Manli Li
,
Shangang Jia
and
Peisheng Mao
*
College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(19), 7521; https://doi.org/10.3390/s22197521
Submission received: 6 September 2022 / Revised: 23 September 2022 / Accepted: 26 September 2022 / Published: 4 October 2022
(This article belongs to the Section Optical Sensors)
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Abstract

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.
Keywords: M. falcata; M. varia; M. sativa; seed identification; stacking ensemble learning; multispectral imaging M. falcata; M. varia; M. sativa; seed identification; stacking ensemble learning; multispectral imaging

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MDPI and ACS Style

Jia, Z.; Sun, M.; Ou, C.; Sun, S.; Mao, C.; Hong, L.; Wang, J.; Li, M.; Jia, S.; Mao, P. Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning. Sensors 2022, 22, 7521. https://doi.org/10.3390/s22197521

AMA Style

Jia Z, Sun M, Ou C, Sun S, Mao C, Hong L, Wang J, Li M, Jia S, Mao P. Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning. Sensors. 2022; 22(19):7521. https://doi.org/10.3390/s22197521

Chicago/Turabian Style

Jia, Zhicheng, Ming Sun, Chengming Ou, Shoujiang Sun, Chunli Mao, Liu Hong, Juan Wang, Manli Li, Shangang Jia, and Peisheng Mao. 2022. "Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning" Sensors 22, no. 19: 7521. https://doi.org/10.3390/s22197521

APA Style

Jia, Z., Sun, M., Ou, C., Sun, S., Mao, C., Hong, L., Wang, J., Li, M., Jia, S., & Mao, P. (2022). Single Seed Identification in Three Medicago Species via Multispectral Imaging Combined with Stacking Ensemble Learning. Sensors, 22(19), 7521. https://doi.org/10.3390/s22197521

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