高光谱成像技术在农业中的应用概述

doi:10.16178/j.issn.0528-9017.20170702

摘要/Abstract

摘要： 高光谱成像技术融合了传统的成像和光谱技术的优点,可以同时获取被检测物体的空间信息和光谱信息,作为一种简便、快捷、光学无损检测的新技术,已经在农业生产中受到广泛关注,并得到应用。本研究在梳理相关文献的基础上,分析了高光谱成像系统的构成,从高光谱成像技术在农畜产品质量和安全检测、农作物生长监测和产量估计、植物营养分析与施肥效果评估等多个方面,综述了高光谱成像技术在农业上的应用现状,并针对其在农业应用上的发展前景进行展望。

关键词: 高光谱, 成像技术, 无损检测, 农业应用

中图分类号:

S127

桂江生, 吴子娴, 顾敏, 迟元峰, 包晓安. 高光谱成像技术在农业中的应用概述[J]. 浙江农业科学, 2017, 58(7): 1101-1105.

参考文献

[1] 孙楠. 探析高技术农业发展的可行性及必要性[J]. 福建农业, 2015(1): 31.
[2] SELVARAJ J N, ZHOU L, WANG Y, et al. Mycotoxin detection——Recent trends at global level[J]. Journal of Integrative Agriculture, 2015, 14(11):2265-2281.
[3] 赵国富, 张喜杰. 基于光谱分析的温室黄瓜营养状态的研究[J]. 农机化研究, 2013(8):18-21.
[4] ADEBAYO S E, HASHIM N, ABDAN K, et al. Application and potential of backscattering imaging techniques in agricultural and food processing-A review[J]. Japan Journal of Food Engineering, 2015, 169:155-164.
[5] MARTINSEN P, SCHAARE P. Measuring soluble solids distribution in kiwifruit using near-infrared imaging spectroscopy[J]. Postharvest Biology & Technology, 1998, 14(3):271-281.
[6] 姚云军, 秦其明, 张自力, 等. 高光谱技术在农业遥感中的应用研究进展[J]. 农业工程学报, 2008, 24(7):301-306.
[7] 任广波, 张杰, 马毅. 基于HJ-1A高光谱的黄河口碱蓬和柽柳盖度反演模型研究[J]. 海洋学报, 2015, 37(9):51-58.
[8] TONG Q, XUE Y, ZHANG L. Progress in hyperspectral remote sensing science and technology in China over the past three decades[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(1):70-91.
[9] HE Z, LIU L. Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2016, 121:11-27.
[10] LI W, DU Q. Laplacian regularized collaborative graph for discriminant analysis of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12):7066-7076.
[11] ZHANG C, LIU F, KONG W, et al. Application of visible and near-infrared hyperspectral imaging to determine soluble protein content in oilseed rape leaves[J]. Sensors, 2015, 15(7):16576-16588.
[12] 李勋兰, 易时来, 何绍兰, 等. 高光谱成像技术的柚类品种鉴别研究[J]. 光谱学与光谱分析, 2015(9):2639-2643.
[13] 郭文川, 董金磊. 高光谱成像结合人工神经网络无损检测桃的硬度[J]. 光学精密工程, 2015, 23(6):1530-1537.
[14] 彭彦昆, 张雷蕾. 农畜产品品质安全高光谱无损检测技术进展和趋势[J]. 农业机械学报, 2013, 44(4):137-145.
[15] NGUYEN-DO-TRONG N, KERESZTES J C, KETELAERE B D, et al. Cross-polarised VNIR hyperspectral reflectance imaging system for agrifood products[J]. Biosystems Engineering, 2016, 151:152-157.
[16] 赵凡, 董金磊, 郭文川. 高光谱图像光谱提取区域对猕猴桃糖度检测精度的影响[J]. 现代食品科技, 2016(4):223-228.
[17] JIANG J, QIAO X, HE R. Use of near-infrared hyperspectral images to identify moldy peanuts[J]. Journal of Food Engineering, 2016, 169:284-290.
[18] SUGIYAMA T, SUGIYAMA J, TSUTA M, et al. NIR spectral imaging with discriminant analysis for detecting foreign materials among blueberries[J]. Journal of Food Engineering, 2010, 101(3):244-252.
[19] RIVERA N V, GÓMEZ-SANCHIS J, CHANONA-PÉREZ J, et al. Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning[J]. Biosystems Engineering, 2014, 122(3):91-98.
[20] XU J L, RICCIOLI C, SUN D W. Efficient integration of particle analysis in hyperspectral imaging for rapid assessment of oxidative degradation in salmon fillet[J]. Journal of Food Engineering, 2015, 169(11):259-271.
[21] SANZ J A, FERNANDES A M, BARRENECHEA E, et al. Lamb muscle discrimination using hyperspectral imaging: Comparison of various machine learning algorithms[J]. Journal of Food Engineering, 2016, 174:92-100.
[22] TAO F, PENG Y, LI Y, et al. Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique[J]. Meat Science, 2012, 90(3):851-857.
[23] KAMRUZZAMAN M, MAKINO Y, OSHITA S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning[J]. Journal of Food Engineering, 2016, 170(7):8-15.
[24] 索少增, 刘翠玲, 吴静珠, 等. 高光谱图像技术检测梨表面农药残留试验研究[J]. 食品科学技术学报, 2011, 29(6):73-77.
[25] 张永贺, 陈文惠, 郭乔影, 等. 桉树叶片光合色素含量高光谱估算模型[J]. 生态学报, 2013, 33(3):876-887.
[26] SENTHILKUMAR T, JAYAS D S, WHITE N D G, et al. Detection of fungal infection and Ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging[J]. Journal of Stored Products Research, 2016, 147:162-173.
[27] 谢传奇, 方孝荣, 邵咏妮, 等. 利用近红外高光谱成像技术检测番茄叶片早疫病[J]. 农业机械学报, 2015, 46(3): 315-319.
[28] 赵芸. 基于高光谱和图像处理技术的油菜病虫害早期监测方法和机理研究[D]. 杭州:浙江大学, 2013.
[29] SIRIPATRAWAN U, MAKINO Y. Monitoring fungal growth on brown rice grains using rapid and non-destructive hyperspectral imaging[J]. International Journal of Food Microbiology, 2015, 199:93-100.
[30] 刘焕军, 康苒, USTIN S, 等. 基于时间序列高光谱遥感影像的田块尺度作物产量预测[J]. 光谱学与光谱分析, 2016, 36(8):2585-2589.
[31] WU Q, QI B, ZHAO T J, et al. A tentative study on utilization of canopy hyperspectral reflectance to estimate canopy growth and seed yield in soybean[J]. Acta Agronomica Sinica, 2013, 39(2):309-318.
[32] 叶旭君, KENSHI S, 何勇. 基于机载高光谱成像的柑橘产量预测模型研究[J]. 光谱学与光谱分析, 2010, 30(5):1295-1300.
[33] BARBEDO J G A, TIBOLA C S, FERNANDES J M C. Detecting Fusarium , head blight in wheat kernels using hyperspectral imaging[J]. Biosystems Engineering, 2015, 131:65-76.
[34] 刘燕德, 肖怀春, 邓清, 等. 柑桔黄龙病近红外光谱无损检测[J]. 农业工程学报, 2016, 32(14):202-208.
[35] 孙俊, 金夏明, 毛罕平, 等. 基于高光谱图像光谱与纹理信息的生菜氮素含量检测[J]. 农业工程学报, 2014, 30(10):167-173.
[36] 朱西存,姜远茂,赵庚星,等. 基于模糊识别的苹果花期冠层钾素含量高光谱估测[J]. 光谱学与光谱分析, 2013, 33(4):1023-1027.
[37] ZHANG X, LIU F, HE Y, et al. Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging[J]. Biosystems Engineering, 2013, 115(1):56-65.
[38] 刘红玉, 毛罕平, 朱文静, 等. 基于高光谱的番茄氮磷钾营养水平快速诊断[J]. 农业工程学报, 2015, 31(增刊1):212-220.