用户名: 密码: 验证码:
水果病虫害无损检测技术研究进展
详细信息    查看全文 | 推荐本文 |
  • 英文篇名:Recent Progress in Technologies for Non-destructive Detection of Fruit Diseases and Pests
  • 作者:乔世成 ; 田有 ; 何宽 ; 姚萍 ; 古文君 ; 王建平
  • 英文作者:QIAO Shicheng;TIAN Youwen;HE Kuan;YAO Ping;GU Wenjun;WANG Jianping;College of Information and Electrical Engineering, Shenyang Agricultral University;College of Computer Science and Technology, Inner Mongolia University for Nationalities;Research Center of Liaoning Agricultural Informatization Engineering Technology;College of Mechanical and Electronic Engineering, Tarim University;
  • 关键词:水果 ; 病害 ; 虫害 ; 无损检测技术 ; 高光谱成像技术
  • 英文关键词:fruit;;diseases;;pests;;non-destructive detection technologies;;hyperspectral imaging technology
  • 中文刊名:SPKX
  • 英文刊名:Food Science
  • 机构:沈阳农业大学信息与电气工程学院;内蒙古民族大学计算机科学与技术学院;辽宁省农业信息化工程技术研究中心;塔里木大学机械电气化工程学院;
  • 出版日期:2018-09-20 14:48
  • 出版单位:食品科学
  • 年:2019
  • 期:v.40;No.600
  • 基金:国家自然科学基金青年科学基金项目(31601219);; 辽宁省科学事业公益研究基金资助项目(20170039);; 内蒙古民族大学科学研究基金资助项目(NMDYB18023)
  • 语种:中文;
  • 页:SPKX201911034
  • 页数:8
  • CN:11
  • ISSN:11-2206/TS
  • 分类号:235-242
摘要
水果的病害和虫害是影响水果品质等级鉴定的重要因素。水果在生长、加工、贮藏、运输过程中容易受到病菌侵染和害虫侵蚀,这将造成水果品质降低,同时对食品安全也会造成不良影响。本文综述了X射线成像技术、计算机视觉技术、核磁共振技术、光谱技术、新兴传感器技术等无损检测技术在水果病虫害识别与检测中的应用进展,并分析各技术的优势和劣势,重点介绍了高光谱成像技术在水果病虫害识别与检测方面的应用进展,分析存在的问题、展望发展趋势,为后续研究提供参考。
        Diseases and pests are important factors affecting fruit quality grading. During the process of growth, processing,storage and transportation, fruit are susceptible to pathogens and pests, reducing fruit quality and causing adverse effects on food safety. Recent progress in the application of X-ray imaging, computer vision, nuclear magnetic resonance, spectroscopy and emerging sensors for the non-destructive detection of fruit diseases and pests is summarized in this article. Furthermore,the advantages and disadvantages of each technology are also analyzed, and particular focus is placed on the application of hyperspectral imaging technique in this field. Finally, existing problems and future directions are proposed in order to provide useful information for further research.
引文
[1]LI B,COBO-MEDINA M,LECOURT J,et al.Application of hyperspectralimaging for nondestructive measurement of plum quality attributes[J].Postharvest Biology and Technology,2018,141:8-15.DOI:10.1016/j.postharvbio.2018.03.008.
    [2]MAGWAZA L S,OPARA U L,CRONJE P J R,et al.Assessment of rind quality of‘Nules Clementine’mandarin fruit during postharvest storage:2.robust Vis/NIRS PLS models for prediction of physicochemical attributes[J].Scientia Horticulturae,2013,165:421-432.DOI:10.1016/j.scienta.2013.09.050.
    [3]GUTHRIE J A,WALSH K B,REID D J,et al.Assessment of internal quality attributes of mandarin fruit.1.NIR calibration model development[J].Austrxalian Journal of Agricultural Research,2005,56(4):405-416.DOI:10.1071/ar04257.
    [4]GADGILE D P,JOSHI C P,SHINDE V M,et al.Detection of green mold rot infection of citrus fruit by X-ray scanning non-destructive technology[J].Current Botany,2017,8:78-80.DOI:10.19071/cb.2017.v8.3211.
    [5]THOMAS P,KANNAN A,DEGWEKAR V H,et al.Non-destructive detection of seed weevil-infested mango fruits by X-ray imaging[J].Postharvest Biology and Technology,1995,5(1/2):161-165.DOI:10.1016/0925-5214(94)00019-O.
    [6]JIANG J A,CHANG H Y,WU K H,et al.An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits[J].Computers and Electronics in Agriculture,2008,60(2):190-200.DOI:10.13031/2013.19162.
    [7]VAN DAEL M V,LEBOTSA S,HERREMANS E,et al.Asegmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs[J].Postharvest Biology and Technology,2016,112:205-214.DOI:10.1016/j.postharvbio.2015.09.020.
    [8]ARENDSE E,FAWOLE O A,MAGWAZA L S,et al.Estimation of the density of pomegranate fruit and their fractions using X-ray computed tomography calibrated with polymeric materials[J].Biosystems Engineering,2016,148:148-156.DOI:10.1016/j.biosystemseng.2016.06.009.
    [9]孙大文,吴迪,何鸿举,等.现代光学成像技术在食品品质快速检测中的应用[J].华南理工大学学报(自然科学版),2012,40(10):59-68.DOI:10.3969/j.issn.1000-565X.2012.10.008.
    [10]LEIVA-VALENZUELA G A,AGUILERA J M.Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality[J].Food Control,2013,1(33):166-173.DOI:10.1016/j.foodcont.2013.02.025.
    [11]STEGMAYER G,MILONE D H,GARRAN S,et al.Automatic recognition of quarantine citrus diseases[J].Expert Systems with Applications,2013,40(9):3512-3517.DOI:10.1016/j.eswa.2012.12.059.
    [12]DUBEY S R,JALAL A S.Adapted approach for fruit disease identification using images[J].Image Processing Concepts Methodologies Tools&Applications,2012,2(3):44-58.
    [13]GAIKWAD D,KARANDE K,DESHPANDE H.Detection of Diseases and grading in pomegranate fruit using digital image processing[J].International Journal of Electronics,Electrical and Computational System,2017,11(6):54-59.
    [14]DUBEY S R,JALAL A S.Detection and classification for apple fruit diseases using support vector machine and chain code[J].International Research Journal of Engineering and Technology,2015,2:2097-2104.
    [15]JHURIA M,KUMAR A,BORSE R.Image processing for smart farming:detection of disease and fruit grading[C]//2013 IEEESecond International Conference on Image Processing(ICIIP-2013).Piscataway,NJ,USA:IEEE Press,2013:521-526.DOI:10.1109/iciip.2013.6707647.
    [16]韩东海,涂润林,刘新鑫,等.鸭梨黑心病与其果皮颜色、硬度和糖度的方差分析[J].农业机械学报,2005,36(3):71-74.
    [17]RONG Dian,RAO Xiuqin,YING Yibin.Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm[J].Computers and Electronics in Agriculture,2017,137:59-68.DOI:10.1016/j.compag.2017.02.027.
    [18]RONG Dian,YING Yibin,RAO Xiuqiu.Embedded vision detection of defective orange by fast adaptive lightness correction algorithm[J].Computers and Electronics in Agriculture,2017,138:48-59.DOI:10.1016/j.compag.2017.03.021.
    [19]温芝元,曹乐平.椪柑果实病虫害的傅里叶频谱重分形图像识别[J].农业工程学报,2013,29(23):159-165.DOI:10.3969/j.issn.1002-6819.2013.23.022.
    [20]温芝元,曹乐平.基于补偿模糊神经网络的脐橙不同病虫害图像识别[J].农业工程学报,2012,28(11):152-157.DOI:10.3969/j.issn.1002-6819.2012.11.025.
    [21]周水琴.基于核磁共振成像的梨果品质无损检测方法研究[D].杭州:浙江大学,2013:79-119.
    [22]GONZALEZ J J,VALLE R C,BOBROFF S,et al.Detection and monitoring of internal browning development in‘Fuji’apples using MRI[J].Postharvest Biology and Technology,2001,22(2):179-188.DOI:10.1016/s0925-5214(00)00183-6.
    [23]HERNáNDEZ-SáNCHEZ N,HILLS B P,BARREIRO P,et al.An NMR study on internal browning in pears[J].Postharvest Biology and Technology,2007,44(3):260-270.DOI:10.1016/j.postharvbio.2007.01.002.
    [24]SUCHANEK M,KORDULSKA M,OLEJNICZAK Z,et al.Application of low-field MRI for quality assessment of‘Conference’pears stored under controlled atmosphere conditions[J].Postharvest Biology and Technology,2017,124:100-106.DOI:10.1016/j.postharvbio.2016.10.010.
    [25]周水琴,应义斌,商德胜.基于形态学的香梨褐变核磁共振成像无损检测[J].浙江大学学报(工学版),2012,46(12):2141-2145.DOI:10.3785/j.issn.1008-973X.2012.12.002.
    [26]张建锋,何勇,龚向阳,等.基于核磁共振成像技术的香梨褐变检测[J].农业机械学报,2013,44(12):169-173;147.DOI:10.6041/j.issn.1000-1298.2013.12.028.
    [27]王淼,张晶,贺妍,等.基于低场核磁共振的柑橘汁胞粒化评级[J].农业工程学报,2016,32(7):290-295.DOI:10.11975/j.issn.1002-6819.2016.07.041.
    [28]KRASZNI M,MAROSI A,LARIVE C K.NMR assignments and the acid-base characterization of the pomegranate ellagitannin punicalagin in the acidic pH-range[J].Analytical and Bioanalytical Chemistry,2013,405(17):5807-5816.DOI:10.1007/s00216-013-6987-x.
    [29]MARCONE M F,WANG S,ALBABISH W,et al.Diverse food-based applications of nuclear magnetic resonance(NMR)technology[J].Food Research International,2013,51(2):729-747.DOI:10.1016/j.foodres.2012.12.046.
    [30]JIE D F,XIE L J,RAO X Q,et al.Using visible and near infrared diffuse transmittance technique to predict soluble solids content of watermelon in an online detection system[J].Postharvest Biology and Technology,2014,90:1-6.DOI:10.1016/j.postharvbio.2013.11.009.
    [31]NICOLA?B M,BEULLENS K,BOBELYN E,et al.Nondestructive measurement of fruit and vegetable quality by means of NIRspectroscopy:a review[J].Postharvest Biology and Technology,2007,46(2):99-118.DOI:10.1016/j.postharvbio.2007.06.024.
    [32]NICOLA?B M,THERON K I,LAMMERTYN J.Kernel PLSregression on wavelet transformed NIR spectra for prediction of sugar content of apple[J].Chemometrics and Intelligent Laboratory Systems,2007,85(2):243-252.DOI:10.1016/j.chemolab.2006.07.001.
    [33]MANIWARA P,NAKANO K,BOONYAKIAT D,et al.The use of visible to near infrared spectroscopy for evaluating passion fruit postharvest quality[J].Food Engineering,2014,143:33-43.DOI:10.1016/j.jfoodeng.2014.06.028.
    [34]XING Juan,GUYER D.Detecting internal insect infestation in tart cherry using transmittance spectroscopy[J].Postharvest Biology and Technology,2008,49(3):411-416.DOI:10.1016/j.postharvbio.2008.03.018.
    [35]XING Juan,GUYER D.Comparison of transmittance and reflectance to detect insect infestation in Montmorency tart cherry[J].Computers and Electronics in Agriculture,2008,64(2):194-201.DOI:10.1016/j.compag.2008.04.012.
    [36]TEERACHAICHAYUT S,TERDWONGWORAKUL A,THANAPASE W,et al.Non-destructive prediction of hardening pericarp disorder in intact mangosteen by near infrared transmittance spectroscopy[J].Journal of Food Engineering,2011,106(3):206-211.DOI:10.1016/j.jfoodeng.2011.05.007.
    [37]韩东海,刘新鑫,赵丽丽,等.苹果水心病的光学无损检测[J].农业机械学报,2004,35(5):143-146.
    [38]韩东海,刘新鑫,鲁超,等.苹果内部褐变的光学无损伤检测研究[J].农业机械学报,2006,37(6):86-88;93.
    [39]章林忠,蔡雪珍,方从兵.近红外光谱定量和定性分析技术在鲜食葡萄果实无损检测中的应用[J].浙江农业学报,2018,30(2):330-338.DOI:10.3969/j.issn.1004-1524.2018.02.21.
    [40]BLANCO M,VILLARROYA I.NIR spectroscopy:a rapid-response analytical tool[J].Trends in Analytical Chemistry,2002,21(4):240-250.DOI:10.1016/s0165-9936(02)00404-1.
    [41]MAGWAZA L S,OPARA U L,TERRY L A,et al.Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of Valencia oranges[J].Journal of Food Composition and Analysis,2013,31:144-154.DOI:10.1016/j.jfca.2013.05.007.
    [42]MAGWAZA L S,FORD H D,CRONJE P J R,et al.Application of optical coherence tomography to nondestructively characterise rind breakdown disorder of‘nules Clementine’mandarins[J].Postharvest Biology and Technology,2013,84:16-21.DOI:10.1016/j.postharvbio.2013.03.019.
    [43]SANAEIFAR A,ZAKIDIZAJI H,JAFARI A,et al.Early detection of contamination and defect in foodstuffs by electronic nose:a review[J].TrAC Trends in Analytical Chemistry,2017,97:257-271.DOI:10.1016/j.trac.2017.09.014.
    [44]LI C Y,HEINEMANN P,SHERRY R.Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection[J].Sensors and Actuators:B.Chemical,2007,125(1):301-310.DOI:10.1016/j.snb.2007.02.027.
    [45]PAN Leiqing,ZHANG Wei,ZHU Na,et al.Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry[J].Food Research International,2014,62:162-168.DOI:10.1016/j.foodres.2014.02.020.
    [46]LI Changying,KREWER G W,JI Pingsheng,et al.Gas sensor array for blueberry fruit disease detection and classification[J].Postharvest Biology and Technology,2010,55(3):144-149.DOI:10.1016/j.postharvbio.2009.11.004.
    [47]孔繁荣,郭文川.发育后期苹果的介电特性与理化特性的关系[J].食品科学,2016,37(9):13-17.DOI:10.7506/spkx1002-6630-201609003.
    [48]张立彬,胥芳,周国君,等.苹果的介电特性与新鲜度的关系研究[J].农业工程学报,1996,12(3):186-190.
    [49]EURINGA F,RUSSB W,WILKE W,et al.Development of an impedance measurement system for the detection of decay of apples[J].Procedia Food Science,2011,1:1188-1194.DOI:10.1016/j.profoo.2011.09.177.
    [50]王若琳,王栋,任小林,等.基于电学特征的苹果水心病无损检测[J].农业工程学报,2018,34(5):129-136.DOI:10.11975/j.issn.1002-6819.2018.05.017.
    [51]李芳,蔡骋,马惠玲,等.基于生物阻抗特性分析的苹果霉心病无损检测[J].食品科学,2013,34(18):197-202.DOI:10.7506/spkx1002-6630-201318040.
    [52]马海军,冯美,张继澍.100 Hz~3.98 MHz下苹果虎皮病果实电特性研究[J].农业机械学报,2010,41(11):105-109.
    [53]马海军,宋长冰,张继澍,等.电激励信号频率对红点病苹果采后电学特性影响[J].农业机械学报,2009,40(10):97-101;96.
    [54]王思玲.基于无损法的苹果水心病判别和货架寿命预测的研究与评价[D].杨凌:西北农林科技大学,2015:16-20.
    [55]蔡骋,李永超,马惠玲,等.基于介电特征选择的苹果内部品质无损分级[J].农业工程学报,2013,29(21):279-287.DOI:10.3969/j.issn.1002-6819.2013.21.035.
    [56]GóMEZ-SANCHIS J,GóMEZ-CHOVA L,ALEIXOS N,et al.Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins[J].Journal of Food Engineering,2008,89(1):80-86.DOI:10.1016/j.jfoodeng.2008.04.009.
    [57]GELADI P,BURGER J,LESTANDER T.Hyperspectral imaging:calibration problems and solutions[J].Chemometrics and Intelligent Laboratory Systems,2004,72(2):209-217.DOI:10.1016/j.chemolab.2004.01.023.
    [58]FOLCH-FORTUNY A,PRATS-MONTALBáN J M,CUBERO S,et al.VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits[J].Chemometrics and Intelligent Laboratory Systems,2016,156:241-248.DOI:10.1016/j.chemolab.2016.05.005.
    [59]QIN Jianwei,BURKS T F,KIM M S,et al.Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method[J].Sensing and Instrumentation for Food Quality and Safety,2008,2(3):168-177.DOI:10.1007/s11694-008-9043-3.
    [60]QIN Jianwei,BURKS T F,RITENOUR M A,et al.Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence[J].Journal of Food Engineering,2009,93(2):183-191.DOI:10.1016/j.jfoodeng.2009.01.014.
    [61]XING Juan,GUYER D,ARIANA D,et al.Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry[J].Sensing Instrumentation for Food Quality Safety,2008,2(3):161-167.DOI:10.1007/s11694-008-9047-z.
    [62]LORENTE D,ALEIXOS N,GóMEZ-SANCHIS J,et al.Selection of optimal wavelength features for decay detection in citrus fruit using the ROC curve and neural networks[J].Food and Bioprocess Technology,2011,6(2):530-541.DOI:10.1007/s11947-011-0737-x.
    [63]HAFF R P,SARANWONG S,THANAPASE W,et al.Automatic image analysis and spot classification for detection of fruitfly infestation in hyperspectral images of mangoes[J].Postharvest Biology and Technology,2013,86:23-28.DOI:10.1016/j.postharvbio.2013.06.003.
    [64]SUN Ye,XIAO Hui,TU Sicong,et al.Detecting decayed peach using a rotating hyperspectral imaging testbed[J].LWT-Food Science and Technology,2018,87:326-332.DOI:10.1016/j.lwt.2017.08.086.
    [65]刘思伽,田有文,张芳,等.采用二次连续投影法和BP人工神经网络的寒富苹果病害高光谱图像无损检测[J].食品科学,2017,38(8):277-282.DOI:10.7506/spkx1002-6630-201708043.
    [66]蔡健荣,王建黑,陈全胜,等.波段比算法结合高光谱图像技术检测柑橘果锈[J].农业工程学报,2009,25(1):127-131.
    [67]张保华,黄文倩,李江波,等.用高光谱成像和PCA检测苹果的损伤和早期腐烂[J].红外与激光工程,2013,42(S2):279-283.
    [68]翁海勇,岑海燕,何勇.直接校正算法的柑橘溃疡病高光谱模型传递[J].光谱学与光谱分析,2018,38(1):235-239.DOI:10.3964/j.issn.1000-0593(2018)01-0235-05.
    [69]黄锋华,张淑娟,杨一,等.油桃外部缺陷的高光谱成像检测[J].农业机械学报,2015,46(11):252-259.DOI:10.6041/j.issn.1000-1298.2015.11.034.
    [70]李江波,王福杰,应义斌,等.高光谱荧光成像技术在识别早期腐烂脐橙中的应用研究[J].光谱学与光谱分析,2012,32(1):142-146.DOI:10.3964/j.issn.1000-0593(2012)01-0142-05.
    [71]李江波,饶秀勤,应义斌,等.基于高光谱成像技术检测脐橙溃疡[J].农业工程学报,2010,26(8):222-228.DOI:10.3969/j.issn.1002-6819.2010.08.038.
    [72]DALE L M,THEWIS A,BOUDRY C,et al.Hyperspectral imaging applications in agriculture and agro-food product qualityand safety control:a review[J].Applied Spectroscopy Reviews,2013,48(2):142-159.DOI:10.1080/05704928.2012.705800.
    [73]SHUKLA A,KOT R.An overview of hyperspectral remote sensing and its application in various disciplines[J].IRA-International Journal of Applied Sciences,2016,5(2):85-90.DOI:10.21013/jas.v5.n2.p4.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700