X-Ray Computer Methods for Studying the Structural Integrity of Seeds and Their Importance in Modern Seed Science

Arkhipov, Mikhail; Priyatkin, N. S.; Гусакова, Л. П.; Потрахов, Н. Н.; Gryaznov, A. Yu.; Bessonov, V. B.; Obodovskii, A. V.; Староверов, Н. Е.

doi:10.1134/s1063784219040030

Cited by 21 publications

(14 citation statements)

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“…Different from multispectral imaging that generates reflectance images, X-ray technique produces transmittance images resulting from short wavelengths that can penetrate seed tissues [ 9 , 38 ]. X-ray tests are well known in detecting internal seed defects, which contributes to predict problems in the future such as physiological abnormalities during imbibition, germination and seedling development [ 8 , 9 , 39 , 40 ]. It has been successfully used to identify insect damages in Triticum aestivum [ 39 , 41 ] and Glycine max seeds [ 42 ], desiccation sensitivity of Quercus rubra seeds [ 43 ], and mechanical damage in Archontophoenix cunninghamii seeds [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, X-ray imaging has proved great potential to collect data from internal structures (e.g. damages in embryo and endosperm) [ 7 , 8 ], because X-rays are short electromagnetic waves (ranging from 0.01 to 10 nm), with high penetrating power [ 9 ]. The correlation of these technologies with data obtained by traditional analytical methods can generate new quality markers [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

et al. 2021

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Background The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

et al. 2021

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“…Different from MSI that generates reflectance images, X-ray image is a transmittance image as a result of short wavelengths (range of 0.01 to 10 nm) that can penetrate the seed tissues [30,31]. X-ray tests are well known in detecting internal defects of seeds, which can help to predict possible problems in the future, such as physiological abnormalities during imbibition, germination or seedling growth [14,[31][32][33]. It has been successfully used to identify seeds damaged by insects in Triticum aestivum [32,34] and Glycine max [35], as well as the desiccation sensitivity of Quercus rubra seeds [36] and mechanical damage in Archontophoenix cunninghamii seeds [37].…”

Section: Discussionmentioning

confidence: 99%

“…While MSI can provide spatial and spectral information about the morphological features (e.g., surface structure, texture and internal chemical composition) [11], the X-ray imaging has proved great potential in evaluating internal and anatomical parameters (e.g., damages in embryo and endosperm) [12][13][14]. The correlation of these parameters with data obtained by traditional analytical methods can indicate the potential of these computerized imaging systems to generate new quality markers of seeds [13].…”

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Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

Bianchini

Mascarin

Silva

et al. 2020

Preprint

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Background: Jatropha curcas is an oilseed plant with great potential for biodiesel production. In agricultural industry, the seed quality is still estimated by manual inspection, using destructive, time-consuming and subjective tests that depend on the seed analyst experience. Recent advances in machine vision combined with artificial intelligence algorithms can provide spatial and spectral information for characterization of biological images, reducing subjectivity and optimizing the analysis process.Results: We present a new method for automatic characterization of jatropha seed quality, based on multispectral imaging (MSI) combined with X-ray imaging. We propose an approach along with X-ray images in order to investigate internal problems such as damages in the embryonic axis and endosperm, considering the fact that seed surface profiles can be negatively affected, but without reaching important internal regions of the seeds. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to classify spatial and spectral patters according to the classes of seed quality. Spectral reflectance signatures in a range of 780 to 970 nm and the X-ray images can efficiently predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: MSI and X-ray images have a strong relationship with physiological performance of Jatropha curcas L. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of jatropha seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

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“…Different from multispectral imaging that generates re ectance images, X-ray technique produces transmittance images resulting from short wavelengths that can penetrate seed tissues [38,39]. X-ray tests are well known in detecting internal seed defects, which contributes to predict problems in the future such as physiological abnormalities during imbibition, germination and seedling development [8,9,40,41]. It has been successfully used to identify insect damages in Triticum aestivum [40,42] and Glycine max seeds [43], desiccation sensitivity of Quercus rubra seeds [44], and mechanical damage in Archontophoenix cunninghamii seeds [45].…”

Section: Discussionmentioning

confidence: 99%

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Bianchini

Mascarin

Silva

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

show abstract

X-Ray Computer Methods for Studying the Structural Integrity of Seeds and Their Importance in Modern Seed Science

Cited by 21 publications

References 10 publications

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

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X-Ray Computer Methods for Studying the Structural Integrity of Seeds and Their Importance in Modern Seed Science

Cited by 21 publications

References 10 publications

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality

Multispectral and X-ray images for characterization&nbsp;of Jatropha curcas L.&nbsp;seed quality

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Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality