概述谷物检测的现状及未来发展方向：将计算机视觉与深度学习相结合

IF 2.701

Journal of Food Science and Technology Pub Date : 2025-08-19 DOI:10.1007/s13197-025-06428-3

Xiao Zhang, Dong Li, Lijun Wang, Min Wu

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引用次数: 0

摘要

粮食质量是政府、企业和消费者共同关注的重要问题之一。在粮食的收获、储存和加工过程中，必须严格控制粮食的质量。传统的粮食质量目测评价在速度和准确性方面存在挑战。现代工业逐渐将计算机视觉技术与深度学习相结合作为粮食质量检测的最佳选择。本文简要介绍了计算机视觉和深度学习，介绍了它们的特点及其在粮食加工中的应用。

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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An overview of the current situation and future development direction of grain detection: taking computer vision combined with deep learning

Grain quality is one of the important issues that governments, enterprises and consumers pay common attention to. In the process of grain harvesting, storage and processing, it is necessary to strictly control the quality of grain. Traditional grain quality evaluation by visual inspection is challenging in terms of speed and accuracy. Modern industry is gradually using computer vision technology combined with deep learning as the best choice for grain quality detection. In this paper, computer vision and deep learning are briefly introduced, and their characteristics and applications in grain processing are introduced.

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来源期刊

Journal of Food Science and Technology

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期刊介绍： The Journal of Food Science and Technology (JFST) is the official publication of the Association of Food Scientists and Technologists of India (AFSTI). This monthly publishes peer-reviewed research papers and reviews in all branches of science, technology, packaging and engineering of foods and food products. Special emphasis is given to fundamental and applied research findings that have potential for enhancing product quality, extend shelf life of fresh and processed food products and improve process efficiency. Critical reviews on new perspectives in food handling and processing, innovative and emerging technologies and trends and future research in food products and food industry byproducts are also welcome. The journal also publishes book reviews relevant to all aspects of food science, technology and engineering.