Abstract
BackgroundThe 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.ResultsThe 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.ConclusionThe 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.
Highlights
Cowpea (Vigna unguiculata) known as black-eyed pea and originated in Africa is a very strategic crop in the world having a total harvested area of 12.3 million hectares with an annual dry seed production of 7.0 Mt and an average yield of 5676 kg ha−1 [1]
To create various seed classes, seeds were divided into sub-samples and were liable to controlled deterioration in which seeds were artificially aged (AA) for different periods to produce enough nonviable seeds required for developing robust classification models and to produce seeds that germinate at different periods
The aged and non-aged seeds exhibited similar spectral patterns in the UV, Vis and near infrared (NIR) regions, spectral data from non-aged seeds showed some variations in terms of reflectance intensity especially in the spectral range from 505 nm to 780 nm compared to the aged seeds (Fig. 2a)
Summary
Cowpea (Vigna unguiculata) known as black-eyed pea and originated in Africa is a very strategic crop in the world having a total harvested area of 12.3 million hectares with an annual dry seed production of 7.0 Mt and an average yield of 5676 kg ha−1 [1]. Seed quality is an important issue for all stakeholders involved in crop production including breeders, producers, traders, variety registration agencies, farmers and distributers. The concept of seed quality is composed of several attributes, including varietal and genetic purity, viability, health, germination capacity, vigor and uniformity [3]. The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. The objective of this study was to investigate the potential of computer vision and multispectral imag‐ ing systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. By using spectral signatures of sin‐ gle cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discri‐ minant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination
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