Enhancing Apple's Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging

TL;DR

This paper proposes a novel apple defect classification method combining visible spectrum and 660 nm spectral imaging, utilizing CNNs to improve accuracy and detect details invisible in standard images, thereby enhancing quality control.

Contribution

It introduces an integrated spectral imaging approach with CNNs for apple defect classification, demonstrating improved accuracy over traditional visible spectrum methods.

Findings

660 nm spectral imaging reveals details not visible in the full visible spectrum

MobileNetV1 achieves 98.80% accuracy with spectral imaging

Spectral range selection slightly outperforms full visible spectrum in classification

Abstract

This study addresses the classification of defects in apples as a crucial measure to mitigate economic losses and optimize the food supply chain. An innovative approach is employed that integrates images from the visible spectrum and 660 nm spectral wavelength to enhance accuracy and efficiency in defect classification. The methodology is based on the use of Single-Input and Multi-Inputs convolutional neural networks (CNNs) to validate the proposed strategies. Steps include image acquisition and preprocessing, classification model training, and performance evaluation. Results demonstrate that defect classification using the 660 nm spectral wavelength reveals details not visible in the entire visible spectrum. It is seen that the use of the appropriate spectral range in the classification process is slightly superior to the entire visible spectrum. The MobileNetV1 model achieves an accuracy of 98.80\% on the validation dataset versus the 98.26\% achieved using the entire visible spectrum. Conclusions highlight the potential to enhance the method by capturing images with specific spectral ranges using filters, enabling more effective network training for classification task. These improvements could further enhance the system's capability to identify and classify defects in apples.