Selection of optimal bands for developing multispectral system for inspecting apples for defects

I. Baek; C. Eggleton; S. A. Gadsden; M. S. Kim

doi:10.1117/12.2520469

30 April 2019 Selection of optimal bands for developing multispectral system for inspecting apples for defects

I. Baek, C. Eggleton, S. A. Gadsden, M. S. Kim

Author Affiliations +

Proceedings Volume 11016, Sensing for Agriculture and Food Quality and Safety XI; 110160F (2019) https://doi.org/10.1117/12.2520469
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Hyperspectral image technology is a powerful tool, but oftentimes the data dimension of hyperspectral images must be reduced for practical purposes, depending on the target and environment. For detecting defects on a variety of apple cultivars, this study used hyperspectral data spanning the visible (400 nm) to near-infrared (1000 nm). This paper presents the preliminary results from the selection of optimal spectral bands within that region, using a sequential feature selection method. The selected bands are used for multispectral detection of apple defects by a classification model developed using support vector machine (SVM). As a result, five optimal wavelengths were selected as key features. When using optimal wavelengths, the accuracy of the SVM and SVM with RBF kernel achieved accuracies over 90% for both the calibration and validation data set. However, the results of SVM with RBF kernel (>80%) based on image was more robust than SVM model (>50%). Moreover, SVM with RBF model classified between bruise and sound regions as well specular. The result from this study showed the feasibility of developing a rapid multispectral imaging system based on key wavelengths.

Citation Download Citation

I. Baek, C. Eggleton, S. A. Gadsden, and M. S. Kim "Selection of optimal bands for developing multispectral system for inspecting apples for defects", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 110160F (30 April 2019); https://doi.org/10.1117/12.2520469

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Hyperspectral imaging

Inspection

Near infrared

Imaging systems

Calibration

Defect inspection

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years