Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures ba...
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Autor*in:	Bosoon Park [verfasserIn] Tae-Sung Shin [verfasserIn] Jeong-Seok Cho [verfasserIn] Jeong-Ho Lim [verfasserIn] Ki-Jae Park [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	blueberry firmness hyperspectral microscopy imaging deep learning cell characterization

Übergeordnetes Werk:	In: Agronomy - MDPI AG, 2012, 12(2021), 1, p 85
Übergeordnetes Werk:	volume:12 ; year:2021 ; number:1, p 85

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/agronomy12010085

Katalog-ID:	DOAJ084824018

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520			\|a Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.
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language	English
source	In Agronomy 12(2021), 1, p 85 volume:12 year:2021 number:1, p 85
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author	Bosoon Park
spellingShingle	Bosoon Park misc blueberry firmness misc hyperspectral microscopy imaging misc deep learning misc cell characterization misc Agriculture misc S Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods
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topic_title	Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods blueberry firmness hyperspectral microscopy imaging deep learning cell characterization
topic	misc blueberry firmness misc hyperspectral microscopy imaging misc deep learning misc cell characterization misc Agriculture misc S
topic_unstemmed	misc blueberry firmness misc hyperspectral microscopy imaging misc deep learning misc cell characterization misc Agriculture misc S
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title	Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods
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title_full	Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods
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title_sort	characterizing hyperspectral microscope imagery for classification of blueberry firmness with deep learning methods
title_auth	Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods
abstract	Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.
abstractGer	Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.
abstract_unstemmed	Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set.
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title_short	Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods
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