Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds

Background The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor inte...
Ausführliche Beschreibung

Background The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. Results The results revealed that the LDA models had good accuracy in distinguishing ‘Aged’ and ‘Non-aged’ seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, ‘Germinated’ and ‘Non-germinated’ seeds with OCC of 81.80, 79.05 and 81.0%, ‘Early germinated’, ‘Medium germinated’ and ‘Dead’ seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give ‘Normal’ and ‘Abnormal’ seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. Conclusion The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality. Ausführliche Beschreibung

Autor*in:	ElMasry, Gamal [verfasserIn] Mandour, Nasser Wagner, Marie-Hélène Demilly, Didier Verdier, Jerome Belin, Etienne Rousseau, David

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Cowpea seeds Black-eyed seeds Plant development Germination Phenotyping Chemometric Multispectral imaging

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Plant methods - London : BioMed Central, 2005, 15(2019), 1 vom: 12. März
Übergeordnetes Werk:	volume:15 ; year:2019 ; number:1 ; day:12 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s13007-019-0411-2

Katalog-ID:	SPR029411025