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Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Authors :
Nader Ekramirad
Alfadhl Y. Khaled
Lauren E. Doyle
Julia R. Loeb
Kevin D. Donohue
Raul T. Villanueva
Akinbode A. Adedeji
Source :
Foods, Vol 11, Iss 1, p 8 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Details

Language :
English
ISSN :
23048158
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Foods
Publication Type :
Academic Journal
Accession number :
edsdoj.4953836aa89845029d50e7753635a9a2
Document Type :
article
Full Text :
https://doi.org/10.3390/foods11010008