The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research

Mortensen, Anders Krogh; Gislum, René; Jørgensen, Johannes Ravn; Boelt, Birte

doi:10.3390/agriculture11040301

Cited by 19 publications

(20 citation statements)

References 67 publications

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“…Few studies provide solutions to maximize peanut seed quality at post-harvest. For other species of agricultural interest, non-destructive technologies that generate data from multispectral images have been successfully used to assess seed quality ( Elmasry et al, 2019a ; Mortensen et al, 2021 ). Considering this possibility, the peanut seed may present unexplored spectral markers that allow the efficient evaluation of this quality.…”

Section: Introductionmentioning

confidence: 99%

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

et al. 2022

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Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F0, Fm, and Fv/Fm) and stress indices (anthocyanin and NDVI). Artificial intelligence features (QDA method) applied to the data extracted from the seed images categorized lots with high and low quality. Higher levels of anthocyanin were found in the leaves of seedlings from low quality seeds. Therefore, this information is promising since the initial behavior of the seedlings reflected the quality of the seeds. The existence of new markers that effectively screen peanut seed quality was confirmed. The combination of physical properties (area, length, width, and coat brightness), pigments (chlorophyll fluorescence and anthocyanin), and light reflectance (660, 690, and 780 nm), is highly efficient to identify peanut seedlots with superior quality (98% accuracy).

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Section: Introductionmentioning

confidence: 99%

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

et al. 2022

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“…Combined with chemometric tools it is widely applied for spectral fingerprints in food analysis 13 , 14 , such as screening of toxic contaminants, like aflatoxins 15 – 17 . Multispectral imaging (MSI) technique, provide simultaneously measuring spectral and spatial information of samples (seeds) by imaging their surface reflectance at selected wavelengths 18 . Applications of the MSI for seed analysis have been previously reported by several works 12 , 18 .…”

Section: Introductionmentioning

confidence: 99%

“…Multispectral imaging (MSI) technique, provide simultaneously measuring spectral and spatial information of samples (seeds) by imaging their surface reflectance at selected wavelengths 18 . Applications of the MSI for seed analysis have been previously reported by several works 12 , 18 . Also, an advantage of this technique is the non-invasive and rapid evaluation of the overall quality parameters of the seeds lots, as well as the individual seeds 12 .…”

Section: Introductionmentioning

confidence: 99%

Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Bartolić

Mutavdžić

Carstensen

et al. 2022

Sci Rep

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Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B1 (AFB1) from the uncontaminated seeds. The AFB1-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB1-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB1-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.

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“…The power of multispectral imaging also extended to predict the germination of alfalfa seeds, especially based on nCDA imaging. This nCDA method can produce the transformed color image quickly to determine seed categories in a convenient and high-throughput way of integrating all the spectral data variations [ 21 , 24 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Wang

Zhang

Song

et al. 2021

Sensors

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Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.

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The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed Testing and Research

Cited by 19 publications

References 67 publications

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

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