| 陈龙跃 1,2，段丹丹 1,2，王 凡 1,2，孟翔宇 1,2，赵 冲 1,2，钱英军 3.高光谱成像技术在水果品质无损检测中的研究进展[J].广东农业科学,2023,50(7):83-94 |
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高光谱成像技术在水果品质无损检测中的研究进展 |
| Research Progress of Non-destructive Testing of Fruit Quality by Hyperspectral Imaging Technology |
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| DOI：10.16768/j.issn.1004-874X.2023.07.009 |
| 中文关键词: 高光谱成像 水果品质 无损检测 机器学习 特征提取 模型训练 |
| 英文关键词: hyperspectral imaging fruit quality non-destructive testing machine learning feature extraction model training |
| 基金项目:岭南现代农业科学与技术广东省实验室河源分中心自主科研项目（DT20220011）；北京市农林科学院专项（CZZJ202203）；广东省科技创新战略专项（210909114530725） |
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| 中文摘要: |
| 优质水果的生产和销售离不开水果品质检测，传统的水果品质检测手段精度低、成本高、时效性差、破坏性强。近年来，随着科学技术的不断进步，低成本、高效率的水果品质无损检测技术得到飞速发展。其中，高光谱成像技术逐渐成为研究热点。综述了该技术在水果品质无损检测方面的技术原理、应用和发展现状，探讨其在水果品质无损检测领域的应用潜力、存在问题、发展趋势以及应用前景。整体来看，高光谱成像技术能够实现不同水果种类、多个水果品质指标的无损、高效检测，如成熟度、糖度、酸度、红色指数等；受硬件技术限制，其发展侧重于数据挖掘方向，即在硬件发展有限的情况下，通过不断更新和优化的针对性算法获得精准的解析结果；另一方面，设备昂贵、数据处理复杂、模型普适性较差是该技术需要进一步优化和改进的主要问题；其未来发展将基于云计算和人工智能的高效数据处理、适用范围更广的水果品质高光谱检测设备研发、多源综合无损检测等研究方向。随着技术的不断发展，高光谱成像技术在水果品质无损检测方面的应用前景广阔，未来将成为水果品质检测的重要手段之一。 |
| 英文摘要: |
| The production and sales of high-quality fruits are inseparable from fruit quality testing, while traditional fruit quality testing methods have low precision, high cost, poor timeliness, and strong destructiveness. With the continuous advancement of science and technology in recent years, low-cost and high-efficiency non-destructive testing technology for fruit quality has been developed rapidly. Among them, hyperspectral imaging technology has gradually become a research hotspot. This paper summarizes the technical principle, application and development status of this technology in the field of fruit quality, and discusses its performance potential, existing problems, development trend and application prospects in the field of non-destructive testing of fruit quality. On the whole,hyperspectral imaging technology can non-destructively and efficiently realize the effective detection of various fruits and various quality indicators, such as maturity, sugar content, acidity,
red index, etc.; Limited by hardware technology, its development focuses on data mining, that is, in the case of limited hardware development, accurate analysis results can be obtained through continuously updated and optimized targeted algorithms; on the other hand, expensive equipment, complex data processing, and poor model universality are the main problems that this technology needs to be further optimized and improved; Its future development will be based on efficient data processing of cloud computing and artificial intelligence, research and development of hyperspectral detection equipment for fruit quality with a wider range of applications, and multi-source comprehensive non-destructive testing. With the continuous development of technology, hyperspectral imaging technology has broad application prospects in non-destructive testing of fruit quality, and will become one of the important means of fruit quality testing in the future. |
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