Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted...

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Bibliographic Details
Published in:PloS one 2016-05, Vol.11 (5), p.e0155523-e0155523
Main Authors: McDonald, Linda S, Panozzo, Joseph F, Salisbury, Phillip A, Ford, Rebecca
Format: Article
Language:English
Subjects:
Agriculture
Belt conveyors
Biology and Life Sciences
Calibration
Classification
Color
Computer and Information Sciences
Digital imaging
Discriminant Analysis
Engineering and Technology
Image Processing, Computer-Assisted
Lasers
Marketing
Markets
Models, Biological
Oryza
Pea
Peas
Physical Sciences
Physiological aspects
Pisum sativum
Pisum sativum - anatomy & histology
Pisum sativum - physiology
Quality
Reproducibility of Results
Research and Analysis Methods
Science
Seeds
Seeds - physiology
Variation (Biology)
Vision systems
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Summary:Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0155523