Summary of Enhancing Apple’s Defect Classification: Insights From Visible Spectrum and Narrow Spectral Band Imaging, by Omar Coello et al.
Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging
by Omar Coello, Moisés Coronel, Darío Carpio, Boris Vintimilla, Luis Chuquimarca
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes an innovative approach for classifying defects in apples by integrating visible spectrum and 660 nm spectral wavelength images. The method employs Single-Input and Multi-Inputs convolutional neural networks (CNNs) to validate the proposed strategies, including image acquisition, preprocessing, classification model training, and performance evaluation. The results demonstrate that defect classification using the 660 nm spectral wavelength reveals details not visible in the entire visible spectrum, with a slight superiority in accuracy compared to the entire visible spectrum. Specifically, the MobileNetV1 model achieves an accuracy of 98.80% on the validation dataset versus 98.26% achieved using the entire visible spectrum. The study highlights the potential to enhance the method by capturing images with specific spectral ranges using filters, enabling more effective network training for classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to better identify and classify defects in apples. This is important because it can help reduce economic losses and improve the food supply chain. The researchers used a special way of taking pictures of apples that uses two different types of light: visible light and a special kind of light with a wavelength of 660 nm. They then used computer models to analyze these pictures and see how well they could identify defects. The results showed that using this special type of light helped them find defects more accurately than just using regular visible light. This could lead to new ways of identifying and classifying defects in apples, which would be helpful for farmers and the food industry. |
Keywords
* Artificial intelligence * Classification
