Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

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Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis. / Salimi, Zahra; Boelt, Birte.

I: Sensors, Bind 19, Nr. 10, 2360, 22.05.2019.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{31cf90ad295644c29cc4c4d016f1b6d4,
title = "Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis",
abstract = "The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.",
keywords = "MECHANICAL DAMAGE, TOOL, WHEAT, machine vision, mechanical damage, prediction model, seed polishing, seed quality",
author = "Zahra Salimi and Birte Boelt",
year = "2019",
month = may,
day = "22",
doi = "10.3390/s19102360",
language = "English",
volume = "19",
journal = "Sensors",
issn = "1424-8220",
publisher = "M D P I AG",
number = "10",

}

RIS

TY - JOUR

T1 - Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

AU - Salimi, Zahra

AU - Boelt, Birte

PY - 2019/5/22

Y1 - 2019/5/22

N2 - The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.

AB - The pericarp of monogerm sugar beet seed is rubbed off during processing in order to produce uniformly sized seeds ready for pelleting. This process can lead to mechanical damage, which may cause quality deterioration of the processed seeds. Identification of the mechanical damage and classification of the severity of the injury is important and currently time consuming, as visual inspections by trained analysts are used. This study aimed to find alternative seed quality assessment methods by evaluating a machine vision technique for the classification of five damage types in monogerm sugar beet seeds. Multispectral imaging (MSI) was employed using the VideometerLab3 instrument and instrument software. Statistical analysis of MSI-derived data produced a model, which had an average of 82% accuracy in classification of 200 seeds in the five damage classes. The first class contained seeds with the potential to produce good seedlings and the model was designed to put more limitations on seeds to be classified in this group. The classification accuracy of class one to five was 59, 100, 77, 77 and 89%, respectively. Based on the results we conclude that MSI-based classification of mechanical damage in sugar beet seeds is a potential tool for future seed quality assessment.

KW - MECHANICAL DAMAGE

KW - TOOL

KW - WHEAT

KW - machine vision

KW - mechanical damage

KW - prediction model

KW - seed polishing

KW - seed quality

UR - http://www.scopus.com/inward/record.url?scp=85066841347&partnerID=8YFLogxK

U2 - 10.3390/s19102360

DO - 10.3390/s19102360

M3 - Journal article

C2 - 31121960

VL - 19

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 10

M1 - 2360

ER -