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

• Published in: Foods Vol. 11; no. 1; p. 8
• Main Authors: Ekramirad, Nader, Khaled, Alfadhl Y, Doyle, Lauren E, Loeb, Julia R, Donohue, Kevin D, Villanueva, Raul T, Adedeji, Akinbode A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21-12-2021; MDPI
• Subjects: Accuracy; Acoustics; Algorithms; Apples; Carotenoids; Chlorophyll; Classification; codling moth; Cultivars; Data processing; Discriminant analysis; feature selection; Food science; Fruits; Hyperspectral imaging; Image processing; Infestation; Learning algorithms; Machine learning; Model accuracy; Near infrared radiation; near-infrared; Pixels; Post-harvest decay; Spectrum analysis; Statistical methods; Vision systems; Wavelength; Wavelengths; United States > US; near-infrared; machine learning; codling moth; apples; hyperspectral imaging; feature selection
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods11010008

Codling moth (CM) (
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.