Tea grading, blending, and matching based on computer vision and deep learning

IF 3.3 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of the Science of Food and Agriculture Pub Date : 2024-12-22 DOI:10.1002/jsfa.14088

Jilong Guo, Kexin Zhang, Selorm Yao-Say Solomon Adade, Jinsu Lin, Hao Lin, Quansheng Chen

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引用次数: 0

Abstract

BACKGROUND

Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This study developed an efficient and non-destructive method for fast tea grading classification, blending ratio evaluation, and sample matching. The method trained a Residual Network (ResNet) model on an enhanced dataset of tea images and used Convolutional Block Attention Module (CBAM) to improve the model's feature-extraction ability.

RESULTS

The enhanced grade classification model achieved 95.05% accuracy for oolong tea and 99.13% accuracy for black tea, outperforming other deep-learning models such as EfficientNet, MobileNet, and VGG16. For oolong tea blends, the model demonstrated greater efficiency than manual evaluation with an average absolute error of 2.26%. In black tea sample matching, the model achieved an average error of 3.34%.

CONCLUSION

These results highlight the importance of attention mechanisms in improving the analysis of images with intricate textures. The integration of deep learning and attention modules enhanced the accuracy and efficiency of tea quality evaluation processes effectively. This study underscores the transformative potential of intelligent classification and analysis methods in modernizing tea production, ensuring higher standards of consistency and quality. © 2024 Society of Chemical Industry.

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基于计算机视觉和深度学习的茶叶分级、混合和匹配。

背景：准确的茶叶调配评估和样品匹配在茶叶生产过程中至关重要。传统方法面临效率和准确性的挑战，这可以通过计算机视觉和深度学习的进步来解决。本研究开发了一种高效、无损的茶叶快速分级、配比评定和样品匹配方法。该方法在增强的茶叶图像数据集上训练残差网络（ResNet）模型，并使用卷积块注意模块（CBAM）提高模型的特征提取能力。结果：增强的品级分类模型对乌龙茶的准确率达到95.05%，对红茶的准确率达到99.13%，优于其他深度学习模型如EfficientNet、MobileNet、VGG16。对于乌龙茶混合物，该模型比人工评价效率更高，平均绝对误差为2.26%。在红茶样本匹配中，模型的平均误差为3.34%。结论：这些结果突出了注意机制在提高复杂纹理图像分析中的重要性。深度学习和注意力模块的集成有效提高了茶叶质量评价过程的准确性和效率。这项研究强调了智能分类和分析方法在现代化茶叶生产中的变革潜力，确保了更高的一致性和质量标准。©2024化学工业学会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Science of Food and Agriculture 工程技术-农业综合

CiteScore

8.10

自引率

4.90%

发文量

634

审稿时长

3.1 months

期刊介绍： The Journal of the Science of Food and Agriculture publishes peer-reviewed original research, reviews, mini-reviews, perspectives and spotlights in these areas, with particular emphasis on interdisciplinary studies at the agriculture/ food interface. Published for SCI by John Wiley & Sons Ltd. SCI (Society of Chemical Industry) is a unique international forum where science meets business on independent, impartial ground. Anyone can join and current Members include consumers, business people, environmentalists, industrialists, farmers, and researchers. The Society offers a chance to share information between sectors as diverse as food and agriculture, pharmaceuticals, biotechnology, materials, chemicals, environmental science and safety. As well as organising educational events, SCI awards a number of prestigious honours and scholarships each year, publishes peer-reviewed journals, and provides Members with news from their sectors in the respected magazine, Chemistry & Industry . Originally established in London in 1881 and in New York in 1894, SCI is a registered charity with Members in over 70 countries.