用660nm光谱提升苹果缺陷识别精度，效果优于全可见光。
Enhancing Apple's Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging
- 融合可见光与660nm窄波段图像，用CNN进行缺陷分类。
- 660nm波段识别准确率达98.80%，高于全可见光的98.26%。
- 适合食品质检、农业智能分选系统研发者参考。
本研究针对苹果缺陷分类问题，旨在减少经济损失并优化食品供应链。提出一种创新方法，结合可见光与660 nm波段图像，提升缺陷分类的准确性和效率。采用单输入与多输入卷积神经网络（CNN）验证策略，流程包括图像采集与预处理、模型训练及性能评估。结果表明，660 nm波段可揭示可见光无法捕捉的缺陷细节，特定波段使用略优于全可见光范围。MobileNetV1模型在验证集上达到98.80%准确率，优于全可见光的98.26%。结论指出，通过滤光片获取特定光谱范围图像，可更有效支持网络训练，进一步提升苹果缺陷识别能力。
原文摘要 · Abstract (English)
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.
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