高光谱成像技术在水果品质检测中的应用研究进展

doi:10.12133/j.smartag.SA202507020

Smart Agriculture ›› 2025, Vol. 7 ›› Issue (5): 52-66.doi: 10.12133/j.smartag.SA202507020

• 专刊--光智农业创新技术与应用 • 上一篇下一篇

高光谱成像技术在水果品质检测中的应用研究进展

张子申¹^,², 程红², 耿文娟¹(), 关军锋²()

^1. 新疆农业大学园艺学院，新疆乌鲁木齐 830000，中国
^2. 河北省农林科学院生物技术与食品科学研究所，河北石家庄 050051，中国

收稿日期:2025-07-13 出版日期:2025-09-30
基金项目:
财政部、农业农村部：国家梨产业技术体系(CARS-28-23); 河北省农林科学院创新专项(2025KJCXZX-XXXK-3)
作者简介:
张子申，博士研究生，研究方向为果树学。E-mail：zhangzishen2@163.com
通信作者:
耿文娟，博士，教授，研究方向为果树种质资源及栽培生理。E-mail：gwj0526@163.com
关军锋，博士，研究员，研究方向为水果品质及其贮藏加工。E-mail：junfeng-guan@263.net

Research Advances in Hyperspectral Imaging Technology for Fruit Quality Assessment

ZHANG Zishen¹^,², CHENG Hong², GENG Wenjuan¹(), GUAN Junfeng²()

^1. College of Horticulture, Xinjiang Agruicultural University, Urumqi 830000, China
^2. Institute of Biotechnology and Food Science, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang 050051, China

Received:2025-07-13 Online:2025-09-30
Foundation items:Ministry of Finance, Ministry of Agriculture and Rural Affairs: Modern Agricultural Industry (Pear) Technology System Project(CARS-28-23); HAAFS Agriculture Science and Technology Innovation Project(2025KJCXZX-XXXK-3)
About author:
ZHANG Zishen, E-mail: zhangzishen2@163.com
Corresponding author:
GENG Wenjuan, E-mail: gwj0526@163.com
GUAN Junfeng, E-mail: junfeng-guan@263.net

摘要/Abstract

摘要：

［目的/意义］ 高光谱成像（Hyperspectral Imaging, HSI）作为一种融合空间与光谱信息的先进检测技术，能够同步获取水果表面纹理特征及连续波段光谱数据，从而实现无损、可视化和在线化评估水果的外观与内在品质指标。尽管现有研究已广泛探讨HSI在农产品无损检测中的通用性，但针对水果领域的系统评述仍显不足。本文拟从场景适配性、技术演变趋势和产业落地瓶颈等维度深入总结HSI在水果品质检测中的应用研究进展。 ［进展］ HSI技术通过无损、快速的光谱成像，显著提高了水果外观品质、表面缺陷、内在品质（如糖分、酸度、水分）及成熟度的检测精度。此外，HSI技术在病害检测、品种鉴别和产地溯源方面也取得了显著进展，为水果质量控制和供应链管理提供了强有力的技术支持。同时，本文用文献计量分析法表明了HSI技术在水果品质检测中的研究热点与发展趋势。 ［结论/展望］ 未来的研究应优化光谱降维方法，以提高模型的效率和准确性，同时探索迁移学习和增量学习，以增强模型的泛化能力；应着力开发轻量化系统硬件和边缘处理能力，推动技术的实际应用，并结合轻量级深度网络与加速模块，实现实时推理加速；加强标准化体系建设，促进成果的共享与广泛应用；通过融合多模态技术，进一步提升检测平台的精度和智能化水平，最终推动HSI技术在水果产业中实现全面的数字化和智能化应用。

关键词: 高光谱成像, 水果, 品质检测, 知识图谱, CiteSpace

Abstract:

[Significance] Hyperspectral imaging (HSI) is an advanced sensing technique that simultaneously acquires high-resolution spatial data and continuous spectral information, enabling non-destructive, real-time evaluation of both external and internal fruit quality attributes. Despite its widespread application in agricultural product assessment, comprehensive reviews specifically addressing fruit quality evaluation using HSI are limited. This paper presents a comprehensive review of recent advancements in the application of HSI technology for fruit quality detection. [Progress] This paper provides a comprehensive review from three key dimensions: scenario adaptability, technological evolution trends, and industrial implementation bottlenecks, with a further analysis of the research outlook in HSI applications for fruit quality assessment. Specifically, by employing non-destructive and rapid spectral imaging techniques, HSI has markedly enhanced the accuracy of assessing various quality parameters, including external appearance, surface defects, internal quality (such as sugar content, acidity, and moisture), and ripeness. Furthermore, significant progress has been achieved in utilizing HSI for disease detection, variety classification, and origin traceability, thereby providing robust technical support for fruit quality control and supply chain management. In addition, bibliometric analysis is utilized to identify key research areas and emerging trends in the application of HSI technology for fruit quality assessment. [Conclusions and Prospects] Future research should focus on optimizing spectral dimensionality reduction techniques to enhance both the efficiency and accuracy of models. Transfer learning and incremental learning approaches should also be explored to improve the models' ability to generalize across various scenarios and fruit types. In parallel, developing lightweight system hardware and strengthening edge processing capabilities will be essential for enabling the practical deployment of HSI technology in real-world applications. Integrating lightweight deep learning networks and acceleration modules will support real-time inference, enhancing processing speed and facilitating faster data analysis. It is also crucial to establish standardized systems and protocols to promote the sharing of research findings and ensure broader application across different industries. Additionally, incorporating multimodal technologies, such as thermal imaging, gas sensors, and visual data, will improve the accuracy and robustness of detection platforms. This integration will allow for more precise and comprehensive assessments of fruit quality, further advancing the digitalization and intelligent application of HSI technology.

Key words: hyperspectral imaging, fruit, quality detection, knowledge graph, CiteSpace

中图分类号:

张子申, 程红, 耿文娟, 关军锋. 高光谱成像技术在水果品质检测中的应用研究进展[J]. 智慧农业(中英文), 2025, 7(5): 52-66.

ZHANG Zishen, CHENG Hong, GENG Wenjuan, GUAN Junfeng. Research Advances in Hyperspectral Imaging Technology for Fruit Quality Assessment[J]. Smart Agriculture, 2025, 7(5): 52-66.

图/表 8

图 1

图2

表 1

图3

图 4

图5

图6

图7

参考文献 100

[1]	丁世春, 马瑞峻, 陈瑜. 基于高光谱成像的水果品质检测研究进展[J]. 江苏农业科学, 2024, 52(15): 16-26.
	DING S C, MA R J, CHEN Y. Research progress on fruit quality detection based on hyperspectral imaging[J]. Jiangsu agricultural sciences, 2024, 52(15): 16-26.
[2]	WANG B, YANG H, LI L L, et al. Non-destructive detection of Cerasus humilis fruit quality by hyperspectral imaging combined with chemometric method[J]. Horticulturae, 2024, 10(5): ID 519.
[3]	谭涛, 冯树南, 温青纯, 等. 高光谱成像技术在水果品质检测中的应用研究进展[J]. 江苏农业科学, 2024, 52(6): 11-18.
	TAN T, FENG S N, WEN Q C, et al. Research progress on application of hyperspectral imaging technology in detection of fruit quality[J]. Jiangsu agricultural sciences, 2024, 52(6): 11-18.
[4]	郭志明, 桑伟兴, 杨忱, 等. 近红外光谱及成像在果品无损检测中的应用[J]. 包装与食品机械, 2024, 42(5): 1-14.
	GUO Z M, SANG W X, YANG C, et al. Application of nondestructive detection of fruit by near-infrared spectroscopy and imaging[J]. Packaging and food machinery, 2024, 42(5): 1-14.
[5]	PATEL D, BHISE S, KAPDI S S, et al. Non-destructive hyperspectral imaging technology to assess the quality and safety of food: A review[J]. Food production, processing and nutrition, 2024, 6(1): ID 69.
[6]	CHEN C M. Science mapping: A systematic review of the literature[J]. Journal of data and information science, 2017, 2(2): 1-40.
[7]	CHEN C M, SONG M. Visualizing a field of research: A methodology of systematic scientometric reviews[J]. PLoS one, 2019, 14(10): ID e0223994.
[8]	ZHANG J L, QUOQUAB F, MOHAMMAD J. Plastic and sustainability: A bibliometric analysis using VOSviewer and CiteSpace[J]. Arab gulf journal of scientific research, 2024, 42(1): 44-67.
[9]	YEUNG A W K. Bibliometric analysis on the literature of monk fruit extract and mogrosides as sweeteners[J]. Frontiers in nutrition, 2023, 10: ID 1253255.
[10]	GONG Z A, ZHI Z H, ZHANG C L, et al. Non-destructive detection of soluble solids content in fruits: A review[J]. Chemistry, 2025, 7(4): ID 115.
[11]	AMORIELLO T, CIORBA R, RUGGIERO G, et al. Vis/NIR spectroscopy and vis/NIR hyperspectral imaging for non-destructive monitoring of apricot fruit internal quality with machine learning[J]. Foods, 2025, 14(2): ID 196.
[12]	LIU J Y, SUN J, WANG Y S, et al. Non-destructive detection of fruit quality: Technologies, applications and prospects[J]. Foods, 2025, 14(12): ID 2137.
[13]	CHENG M F, MUKUNDAN A, KARMAKAR R, et al. Modern trends and recent applications of hyperspectral imaging: A review[J]. Technologies, 2025, 13(5): ID 170.
[14]	WAN G L, HE J G, MENG X H, et al. Hyperspectral imaging technology for nondestructive identification of quality deterioration in fruits and vegetables: A review[J]. Critical reviews in food science and nutrition, 2025: 1-30.
[15]	VIGNATI S, TUGNOLO A, GIOVENZANA V, et al. Hyperspectral imaging for fresh-cut fruit and vegetable quality assessment: Basic concepts and applications[J]. Applied sciences, 2023, 13(17): ID 9740.
[16]	LAN W J, HUI X, NICOLAÏ B, et al. Visualizing the structural and chemical heterogeneity of fruit and vegetables using advanced imaging techniques: Fundamentals, instrumental aspects, applications and future perspectives[J]. Critical reviews in food science and nutrition, 2025, 65(21): 4147-4171.
[17]	KIM G, LEE H, CHO B K, et al. Quantitative evaluation of food-waste components in organic fertilizer using visible–near-infrared hyperspectral imaging[J]. Applied sciences, 2021, 11(17): ID 8201.
[18]	ALTIERI G, LAVEGLIA S, RASHVAND M, et al. Portable NIR spectroscopy combined with machine learning for kiwi ripeness classification: An approach to precision farming[J]. Applied sciences, 2025, 15(11): ID 6233.
[19]	SHAO Y Y, WANG Y X, XUAN G T. In-field and non-invasive determination of internal quality and ripeness stages of Feicheng peach using a portable hyperspectral imager[J]. Biosystems engineering, 2021, 212: 115-125.
[20]	谢宇彤, 牟涛涛, 陈少华. 高光谱成像技术在精准农业领域的应用方法综述[J]. 传感器世界, 2025, 31(1): 1-10.
	XIE Y T, MU T T, CHEN S H. A review of the application methods of hyperspectral technology in precision agriculture[J]. Sensor world, 2025, 31(1): 1-10.
[21]	XU S, WANG H T, LIANG X, et al. Research progress on methods for improving the stability of non-destructive testing of agricultural product quality[J]. Foods, 2024, 13(23): ID 3917.
[22]	SABOURI A, BAKHSHIPOUR A, POORSALEHI M, et al. Machine learning techniques for non-destructive estimation of plum fruit weight[J]. Scientific reports, 2025, 15: ID 751.
[23]	ZHOU C L, YU Y S, AI J Y, et al. Fruit wines classification enabled by combing machine learning with comprehensive volatiles profiles of GC-TOF/MS and GC-IMS[J]. Food research international, 2025, 204: ID 115890.
[24]	OH H, MENGIST M F, MA G Y, et al. Unraveling the genetic architecture of blueberry fruit quality traits: Major loci control organic acid content while more complex genetic mechanisms control texture and sugar content[J]. BMC plant biology, 2025, 25(1): ID 36.
[25]	GU Y Q, WU J H, GUO Y J, et al. Grade classification of Camellia seed oil based on hyperspectral imaging technology[J]. Foods, 2024, 13(20): ID 3331.
[26]	KHAKI S, WANG L Z, ARCHONTOULIS S V. A CNN-RNN framework for crop yield prediction[J]. Frontiers in plant science, 2019, 10: ID 1750.
[27]	JOSHI A, PRADHAN B, CHAKRABORTY S, et al. An explainable Bi-LSTM model for winter wheat yield prediction[J]. Frontiers in plant science, 2024, 15: ID 1491493.
[28]	CHUN S W, SONG D J, LEE K H, et al. Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging[J]. Postharvest biology and technology, 2024, 214: ID 112918.
[29]	OLISAH C C, TREWHELLA B, LI B, et al. Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment[J]. Engineering applications of artificial intelligence, 2024, 132: ID 107945.
[30]	ZHAO Y Y, ZHOU L, WANG W, et al. Visible/near-infrared spectroscopy and hyperspectral imaging facilitate the rapid determination of soluble solids content in fruits[J]. Food engineering reviews, 2024, 16(3): 470-496.
[31]	WENG S Z, YU S, GUO B Q, et al. Non-destructive detection of strawberry quality using multi-features of hyperspectral imaging and multivariate methods[J]. Sensors, 2020, 20(11): ID 3074.
[32]	GAO S, XU J H. Hyperspectral image information fusion-based detection of soluble solids content in red globe grapes[J]. Computers and electronics in agriculture, 2022, 196: ID 106822.
[33]	HASANZADEH B, ABBASPOUR-GILANDEH Y, SOLTANI-NAZARLOO A, et al. Non-destructive detection of fruit quality parameters using hyperspectral imaging, multiple regression analysis and artificial intelligence[J]. Horticulturae, 2022, 8(7): ID 598.
[34]	KIM M J, YU W H, SONG D J, et al. Prediction of soluble-solid content in Citrus fruit using visible–near-infrared hyperspectral imaging based on effective-wavelength selection algorithm[J]. Sensors, 2024, 24(5): ID 1512.
[35]	MENG Q L, TAN T, FENG S N, et al. Prediction and visualization map for physicochemical indices of kiwifruits by hyperspectral imaging[J]. Frontiers in nutrition, 2024, 11: ID 1364274.
[36]	XIAO Y X, ZHAI Y N, ZHOU L, et al. Detection of soluble solid content in Citrus fruits using hyperspectral imaging with machine and deep learning: A comparative study of two Citrus cultivars[J]. Foods, 2025, 14(12): ID 2091.
[37]	GAO W J, CHENG X, LIU X H, et al. Apple firmness detection method based on hyperspectral technology[J]. Food control, 2024, 166: ID 110690.
[38]	WANG Q, LU J Z, WANG Y H, et al. In situ nondestructive identification of Citrus fruit ripeness via hyperspectral imaging technology[J]. Plant methods, 2025, 21(1): ID 77.
[39]	DAI C X, SUN J, HUANG X Y, et al. Application of hyperspectral imaging as a nondestructive technology for identifying tomato maturity and quantitatively predicting lycopene content[J]. Foods, 2023, 12(15): ID 2957.
[40]	SIRIPATRAWAN U, MAKINO Y. Hyperspectral imaging coupled with machine learning for classification of anthracnose infection on mango fruit[J]. Spectrochimica acta part A: Molecular and biomolecular spectroscopy, 2024, 309: 123825.
[41]	ZHU H Y, QIN S, LIANG S K, et al. Hyperspectral imaging and machine learning for quality assessment of apples with different bagging types[J]. Spectrochimica acta part A: Molecular and biomolecular spectroscopy, 2025, 343: ID 126443.
[42]	LI B C, HOU B L, ZHANG D W, et al. Pears characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging[J]. Optik, 2016, 127(5): 2624-2630.
[43]	FENG R Z, HAN X, LAN Y B, et al. Detection of early subtle bruising in strawberries using VNIR hyperspectral imaging and deep learning[J]. Vibrational spectroscopy, 2025, 138: ID 103786.
[44]	Qi H, Li H, Chen L, et al. Hyperspectral imaging using a convolutional neural network with transformer for the soluble solid content and pH prediction of cherry tomatoes[J]. Foods, 2024, 13(2): ID 251.
[45]	LIU J L, MENG H B. Research on the maturity detection method of Korla pears based on hyperspectral technology[J]. Agriculture, 2024, 14(8): ID 1257.
[46]	MENG Q L, FENG S N, TAN T, et al. Application of hyperspectral imaging and chemometrics for determining quality and maturity of loquats[J]. Journal of food safety, 2024, 44(4): ID e13159.
[47]	ANJALI, JENA A, BAMOLA A, et al. State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: Principles, applications, and future directions[J]. Food production, processing and nutrition, 2024, 6(1): ID 56.
[48]	LIU Y F, ZHANG Z, FU Y, et al. Assessment of apple soluble sugar content by hyperspectral vegetation indexes[J]. Spectroscopy letters, 2024, 57(8): 443-451.
[49]	白大昱, 史庆华, 王建全, 等. 基于高光谱特征提取的甜瓜白粉病早期识别[J]. 中国农机化学报, 2024, 45(11): 172-177.
	BAI D Y, SHI Q H, WANG J Q, et al. Early identification of melon powdery mildew based on hyperspectral feature extraction[J]. Journal of Chinese agricultural mechanization, 2024, 45(11): 172-177.
[50]	FAQEERZADA M A, KIM Y N, KIM H, et al. Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit[J]. Postharvest biology and technology, 2024, 218: ID 113151.
[51]	YU X, NING H X, ZHANG W, et al. Nondestructive determination of soluble solids content in apples using self-designed portable visible/near-infrared diffuse transmittance spectrometer combined with feature wavelength selection algorithms[J]. Journal of food composition and analysis, 2025,148: ID 108470.
[52]	MA J, LI M J, FAN W P, et al. State-of-the-art techniques for fruit maturity detection[J]. Agronomy, 2024, 14(12): ID 2783.
[53]	吴虹璋, 蔡红星, 任玉, 等. 基于可见/近红外光谱对葡萄可溶性固形物无损检测研究[J]. 光散射学报, 2024, 36(1): 44-51.
	WU H Z, CAI H X, REN Y, et al. Non-destructive detection of soluble solid content based on visible-near infrared spectroscopy[J]. The journal of light scattering, 2024, 36(1): 44-51.
[54]	PANSY D L, MURALI M. UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN[J]. Environmental monitoring and assessment, 2023, 195(9): ID 1120.
[55]	周旭, 杨倩倩, 张进, 等. 基于便携式近红外光谱仪的黄桃腐败时间快速预测[J]. 食品与机械, 2024, 40(5): 101-106, 187.
	ZHOU X, YANG Q Q, ZHANG J, et al. Rapid prediction of yellow peach spoilage time based on portable near infrared spectrometer[J]. Food & machinery, 2024, 40(5): 101-106, 187.
[56]	班兆军, 高喧翔, 马肄恒, 等. 基于高光谱和深度学习的苹果品质无损检测方法[J]. 江苏农业学报, 2024, 40(8): 1446-1454.
	BAN Z J, GAO X X, MA Y H, et al. Non-destructive detection method of apple quality based on hyperspectral and deep learning[J]. Jiangsu journal of agricultural sciences, 2024, 40(8): 1446-1454.
[57]	BACA-BOCANEGRA B, ESPINAR GARCÍA A I, HERNÁNDEZ-HIERRO J M, et al. Feasibility assessment on the use of near infrared hyperspectral imaging for the screening of vitamin C content and total soluble solids in strawberries[J]. Spectrochimica acta part A: Molecular and biomolecular spectroscopy, 2025, 343: ID 126542.
[58]	LI X L, WEI Z X, PENG F F, et al. Non-destructive prediction and visualization of anthocyanin content in mulberry fruits using hyperspectral imaging[J]. Frontiers in plant science, 2023, 14: ID 1137198.
[59]	LADO J, GAMBETTA G, ZACARIAS L. Key determinants of citrus fruit quality: Metabolites and main changes during maturation[J]. Scientia horticulturae, 2018, 233(2): 238-248.
[60]	ZHANG Z S, CHENG H, CHEN M Y, et al. Detection of pear quality using hyperspectral imaging technology and machine learning analysis[J]. Foods, 2024, 13(23): ID 3956.
[61]	CHENG H, ZHANG Z S, FENG Y X, et al. Nondestructive evaluation of yellowing and senescence in 'Yali' pear using integrated hyperspectral and chlorophyll fluorescence imaging[J]. Food research international, 2025, 209: ID 116254.
[62]	TSAKIRIDIS N L, SAMARINAS N, KOKKAS S, et al. In situ grape ripeness estimation via hyperspectral imaging and deep autoencoders[J]. Computers and electronics in agriculture, 2023, 212: ID 108098.
[63]	ZHAO C Y, REN Z, LI Y, et al. Growth stages discrimination of multi-cultivar navel oranges using the fusion of near-infrared hyperspectral imaging and machine vision with deep learning[J]. Agriculture, 2025, 15(14): ID 1530.
[64]	TIAN X, LIU X F, HE X, et al. Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm[J]. Journal of the science of food and agriculture, 2023, 103(13): 6689-6705.
[65]	LI W X, ZHOU B K, ZHOU Y Z, et al. Grape disease detection using transformer-based integration of vision and environmental sensing[J]. Agronomy, 2025, 15(4): ID 831.
[66]	WU J, LIU C L, OUYANG A G, et al. Early detection of slight bruises in yellow peaches (Amygdalus persica) using multispectral structured-illumination reflectance imaging and an improved ostu method[J]. Foods, 2024, 13(23): ID 3843.
[67]	陈欣欣, 郭辰彤, 张初, 等. 高光谱成像技术的库尔勒梨早期损伤可视化检测研究[J]. 光谱学与光谱分析, 2017, 37(1): 150-155.
	CHEN X X, GUO C T, ZHANG C, et al. Visual detection study on early bruises of Korla pear based on hyperspectral imaging technology[J]. Spectroscopy and spectral analysis, 2017, 37(1): 150-155.
[68]	MATESE A, HAMIE N, BARONTI S, et al. Integrating UAV hyperspectral imaging with machine learning techniques to predict tomato ecophysiological parameters and yield[J]. Precision agriculture, 2025, 26(4): ID 72.
[69]	LAN W J, JAILLAIS B, RENARD C M G C, et al. A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices[J]. Postharvest biology and technology, 2021, 175: ID 111497.
[70]	PETERS M S, AHLEBÆK M J, FRANDSEN M T, et al. Investigating the applicability of a snapshot Computed Tomography Imaging Spectrometer for the prediction of[J]. Spectrochimica acta part A: Molecular and biomolecular spectroscopy, 2025, 336: ID 126017.
[71]	XIAO H H, LI C L, WANG M Y, et al. Nutrient content prediction and geographical origin identification of bananas by combining hyperspectral imaging with chemometrics[J]. Foods, 2024, 13(22): ID 3631.
[72]	ZHAN C Y, MAO H Y, FAN R S, et al. Detection of apple sucrose concentration based on fluorescence hyperspectral image system and machine learning[J]. Foods, 2024, 13(22): ID 3547.
[73]	LI C, JIN C, ZHAI Y N, et al. Simultaneous detection of Citrus internal quality attributes using near-infrared spectroscopy and hyperspectral imaging with multi-task deep learning and instrumental transfer learning[J]. Food chemistry, 2025, 481: ID 143996.
[74]	KANWAL N, KÄMPER W, FARRAR M B, et al. Rapid assessment of lychee and mango fruit quality using hyperspectral imaging[J]. LWT, 2025, 224: ID 117833.
[75]	DATTA D, MALLICK P K, BHOI A K, et al. Hyperspectral image classification: Potentials, challenges, and future directions[J]. Computational intelligence and neuroscience, 2022, 2022(1): ID 3854635.
[76]	ZHANG J G, SU R M, FU Q, et al. A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging[J]. Scientific reports, 2022, 12: ID 11905.
[77]	SZECHYŃSKA-HEBDA M, HOŁOWNICKI R, DORUCHOWS KI G, et al. Application of hyperspectral imaging for early detection of pathogen-induced stress in cabbage as case study[J]. Agronomy, 2025, 15(7): ID 1516.
[78]	BENELLI A, CEVOLI C, RAGNI L, et al. In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging[J]. Biosystems engineering, 2021, 207: 59-67.
[79]	LIANG D D, WANG N, YIN H, et al. Assessment of the optical response of bruised kiwifruit using hyperspectral imaging and its relationships with water migration[J]. Postharvest biology and technology, 2025, 225: ID 113515.
[80]	SAID A G, JOSHI B. SmartRipen: LSTM-GRU feature selection& XGBoost-CNN for fruit ripeness detection[J]. Food physics, 2025, 2: ID 100053.
[81]	HUANG W N, NIE Y T, ZHU N, et al. Hybrid label-free molecular microscopies for simultaneous visualization of changes in cell wall polysaccharides of peach at single- and multiple-cell levels during postharvest storage[J]. Cells, 2020, 9(3): ID 761.
[82]	SZYMAŃSKA-CHARGOT M, CHYLIŃSKA M, PIECZYWEK P M, et al. Raman imaging of changes in the polysaccharides distribution in the cell wall during apple fruit development and senescence[J]. Planta, 2016, 243(4): 935-945.
[83]	MINAS I S, BLANCO-CIPOLLONE F, STERLE D. Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy[J]. Food chemistry, 2021, 335: ID 127626.
[84]	GAO Q, WANG P, NIU T, et al. Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging[J]. Food chemistry, 2022, 370: ID 131013.
[85]	OUYANG H K, TANG L L, MA J L, et al. Application of hyperspectral technology with machine learning for Brix detection of pastry pears[J]. Plants, 2024, 13(8): ID 1163.
[86]	SU Z Z, ZHANG C, YAN T Y, et al. Application of hyperspectral imaging for maturity and soluble solids content determination of strawberry with deep learning approaches[J]. Frontiers in plant science, 2021, 12: ID 736334.
[87]	WENG S Z, YU S, DONG R L, et al. Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging[J]. International journal of food properties, 2020, 23(1): 269-281.
[88]	KHALED A Y, EKRAMIRAD N, DONOHUE K D, et al. Non-destructive hyperspectral imaging and machine learning-based predictive models for physicochemical quality attributes of apples during storage as affected by codling moth infestation[J]. Agriculture, 2023, 13(5): ID 1086.
[89]	SIEDLISKA A, BARANOWSKI P, ZUBIK M, et al. Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging[J]. Postharvest biology and technology, 2018, 139: 115-126.
[90]	ZHU G Q, ZHENG S G, XU Q S, et al. Detection of fungal infection in apple using hyperspectral transformation of RGB images with kernel regression[J]. Postharvest biology and technology, 2023, 206: ID 112570.
[91]	MIN D D, ZHAO J S, BODNER G, et al. Early decay detection in fruit by hyperspectral imaging-Principles and application potential[J]. Food control, 2023, 152: ID 109830.
[92]	CHENG H, ZHANG Z S, CHENG Y D, et al. Potential of hyperspectral imaging for nondestructive determination of α-farnesene and conjugated trienol content in 'Yali' pear[J]. Spectrochimica acta part A: Molecular and biomolecular spectroscopy, 2024, 321: ID 124688.
[93]	OU Y M, YAN J Y, LIANG Z Y, et al. Hyperspectral imaging combined with deep learning for the early detection of strawberry leaf gray mold disease[J]. Agronomy, 2024, 14(11): ID 2694.
[94]	LI X L, PENG F F, WEI Z X, et al. Identification of yellow vein clearing disease in lemons based on hyperspectral imaging and deep learning[J]. Frontiers in plant science, 2025, 16: ID 1554514.
[95]	PÉREZ-RONCAL C, LÓPEZ-MAESTRESALAS A, LOPEZ-MOLINA C, et al. Hyperspectral imaging to assess the presence of powdery mildew (Erysiphe necator) in cv. carignan noir grapevine bunches[J]. Agronomy, 2020, 10(1): ID 88.
[96]	HUANG Y P, YANG Y T, SUN Y, et al. Identification of apple varieties using a multichannel hyperspectral imaging system[J]. Sensors, 2020, 20(18): ID 5120.
[97]	LÓPEZ A, OGAYAR C J, FEITO F R, et al. Classification of grapevine varieties using UAV hyperspectral imaging[J]. Remote sensing, 2024, 16(12): ID 2103.
[98]	BU Y P, HU J X, CHEN C, et al. ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness[J]. Scientific reports, 2024, 14: ID 2568.
[99]	REVELOU P K, TSAKALI E, BATRINOU A, et al. Applications of machine learning in food safety and HACCP monitoring of animal-source foods[J]. Foods, 2025, 14(6): ID 922.
[100]	MORABITO A, DE SIMONE G, PASTORELLI R, et al. Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: A narrative review[J]. Journal of translational medicine, 2025, 23(1): ID 425.

模型类别	算法名称	应用类型	典型应用场景	优势特点
回归预测	PLSR	定量预测	糖度^［30］、酸度^［31］、水分^［32］等理化指标预测	稳定性强，适用于高维小样本数据
	MLR	定量预测	简单质量属性的预测分析^{［30， 33］}	模型结构简单，计算效率高
	RF	定量预测	果实多指标并行预测，提升模型泛化能力^{［34， 35］}	鲁棒性好，抗噪性能强
	XGBoost	定量预测	多品质指标的高精度建模与优化^{［36， 37］}	精度高，适用于复杂非线性回归任务
分类识别	SVM	分类识别	成熟度等级划分^［38］、品种识别^［39］、病害分类^［40］	分类精度高，适合高维数据
	PCA + 判别模型	特征压缩+分类	波段降维后结合判别模型用于品种或缺陷识别^{［41， 42］}	有效降维，提高建模效率
	CNN	深度分类	果面病斑识别^［28］、缺陷检测^［43］、复杂图像分类任务^［29］	能提取空间+光谱联合特征，表现优于浅层模型
	LSTM	分类识别	预测水果成熟度^［44］、检测病害^［26］、评估贮藏期质量变化，以及进行品种鉴别与溯源^［27］	处理时序特征能力强，适用于动态光谱分析任务

高光谱成像技术在水果品质检测中的应用研究进展

Research Advances in Hyperspectral Imaging Technology for Fruit Quality Assessment

在线阅读

知网下载

本地下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 100

相关文章 13

编辑推荐

Metrics

本文评价

[1]	李飞, 王自强, 武晶, 辛霞, 李春梅, 徐虎博. 基于半监督深度卷积生成对抗网络的不平衡自然老化大豆种质活力高光谱检测[J]. 智慧农业(中英文), 2025, 7(5): 101-113.
[2]	于心圆, 王振杰, 尤思聪, 屠康, 兰维杰, 彭菁, 朱丽霞, 陈涛, 潘磊庆. 基于高光谱和X-ray CT的苹果水心病无损检测方法[J]. 智慧农业(中英文), 2025, 7(4): 108-118.
[3]	白瑞斌, 王慧, 王宏鹏, 洪家顺, 周骏辉, 杨健. 基于高光谱成像技术的山楂含水量快速无损分析方法[J]. 智慧农业(中英文), 2025, 7(4): 95-107.
[4]	杨晨雪, 李娴, 周清波. 知识图谱驱动下粮食生产大数据应用现状与展望[J]. 智慧农业(中英文), 2025, 7(2): 26-40.
[5]	袁欢, 范蓓蕾, 杨晨雪, 李娴. 图神经网络应用于知识图谱构建：研究进展、农业发展潜力及未来方向[J]. 智慧农业(中英文), 2025, 7(2): 41-56.
[6]	乔磊, 陈雷, 袁媛. 基于双意图建模和知识图谱扩散的水稻品种选育推荐方法[J]. 智慧农业(中英文), 2025, 7(2): 73-80.
[7]	姜京池, 闫莲, 刘劼. 基于精准知识筛选及知识协同生成的农业大语言模型[J]. 智慧农业(中英文), 2025, 7(1): 20-32.
[8]	郭威, 吴华瑞, 郭旺, 顾静秋, 朱华吉. 特色农产品设施环境下品质智能管控技术研究现状与展望[J]. 智慧农业(中英文), 2024, 6(6): 44-62.
[9]	赵春江. 农业知识智能服务技术综述[J]. 智慧农业(中英文), 2023, 5(2): 126-148.
[10]	商枫楠, 周学成, 梁英凯, 肖明玮, 陈桥, 罗陈迪. 基于改进YOLOX的自然环境中火龙果检测方法[J]. 智慧农业(中英文), 2022, 4(3): 120-131.
[11]	耿闻轩, 赵俊晔, 阮继伟, 侯跃辉. 人工智能辅助种植策略对温室草莓生产调控效果对比研究[J]. 智慧农业(中英文), 2022, 4(2): 183-193.
[12]	张宇, 赵春江, 林森, 郭文忠, 文朝武, 龙洁花. 基于Penman-Monteith模型和路径排序算法相结合的草莓灌溉方法与验证[J]. 智慧农业(中英文), 2021, 3(3): 116-128.
[13]	李亮德, 王秀娟, 康孟珍, 华净, 樊梦涵. 基于语义融合与模型蒸馏的农业实体识别[J]. 智慧农业(中英文), 2021, 3(1): 118-128.