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Received：09 June 2020，
Published：07 January 2021
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作物病虫害作为影响农作物品质、产量及威胁粮食安全的主要因素，仅依靠人工田间调查对其进行监测已不能满足当下农业生产精准高效的需求。高光谱遥感作为能够获取地表物体连续波谱信息的遥感技术，已经成为当下作物病虫害监测识别的重要手段。本文对作物病虫害高光谱遥感监测识别的研究进展进行综述，通过对该领域发表文献的统计以及对主要机构、团队、数据源的分析，明确了病虫害高光谱遥感监测的研究热点和趋势；在此基础上，分析高光谱技术及其作物病虫害的监测识别机理，从病虫害胁迫探测、分类识别、危害严重度定量分析及早期检测四个方面综述相关技术及研究现状；通过探讨当下高光谱遥感病虫害监测识别面临的挑战，提出作物病虫害标准图谱库的建立、星载高光谱传感器的完善以及星空地一体化监测平台的搭建是当前作物病虫害高光谱遥感监测识别技 术落到实处的关键，也是未来发展的重点方向。
The changes in global climate and the accelerated development of trade have continuously expanded the distributions
host ranges
and impacts of crop diseases and pests. They have become one of the most important threatening factors of crop quality
yield
and food safety in the whole process of agricultural production. The monitoring and identification of crop diseases and pests are always based on visual inspection. However
the artificial-based method is time and labor consuming
and the survey results cannot satisfy the requirements of large area and exact analysis. Biological and chemical-related professional bacteria detection method is also costly and unsuitable for promotion in farmers. Remote sensing
which is a typical non-invasive method
provides reliable and precise technical support for real-time and large-scale monitoring of crop diseases and pests in recent decades. Each remote sensing system
such as visible and near-infrared spectral sensors
fluorescence and thermal sensors
and synthetic aperture radar and light detection and ranging system
has its own characteristics and maturity in detecting and monitoring plant diseases and pests. Hyperspectral remote sensing technology can easily
quickly
non-destructively
and accurately assess information of diseases and pests
including type identification
detection
mapping
and severity and loss assessment
because of its continuous narrow waveband characteristics.
The occurrence of crop diseases and pests is a dynamic and complex process. On the one hand
crop diseases and pests are often caused by more than one causal agent
and each has different symptoms. On the other hand
host plant pathogen and pest interaction is a complex dynamic process with changes in various physiological and biochemical parameters. The two main aspects make the application of hyperspectral technology in the monitoring of diseases and pests particularly prominent because it can cover a spectral range of up to 350—2500 nm and can yield a narrow spectral resolution of less than 10 nm. These characteristics are suitable not only for disease differentiation based on slight differences but also for monitoring and analysis of dynamic disease processes. This extra information will provide additional benefits for plant disease detection
especially for detection during the latency period when symptoms are invisible to the human eye.
This review first describes the basic principles of hyperspectral remote sensing and introduces the investigating mechanism of crop diseases and pests. On the basis of bibliometric analysis on the hyperspectral remote sensing-based monitoring of crop diseases and pests and detection literature from WOS and CNKI
four main research directions are summarized: identification of diseases and pest and healthy crops
classification of different diseases and pests
quantitative analysis of severity
and early asymptomatic detection. Then
we review the main development of related technologies and research status in detail. Finally
three major challenges are put forward on the basis of the abovementioned summary on technologies
developments
advantages
and disadvantages of monitoring of crop diseases and pests. This review proves that the establishment of standard spectral library of crop diseases and pests on different scales
the improvement of satellite hyperspectral sensors
and the construction of the integrated monitoring platform will be the key points to applying hyperspectral remote sensing technology.
Abdulridha J , Ampatzidis Y , Ehsani R and de Castro A I . 2018 . Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado . Computers and Electronics in Agriculture , 155 : 203 - 211 [ DOI: 10.1016/j.compag.2018.10.016 http://dx.doi.org/10.1016/j.compag.2018.10.016 ]
Adam E , Deng H , Odindi J , Abdel-Rahman E M , Mutanga O . 2017 . Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in situ hyperspectral data and guided regularized random forest algorithm . Journal of Spectroscopy , 2017 , 1-8 [ DOI: 10.1155/2017/6961387 http://dx.doi.org/10.1155/2017/6961387 ]
Afonso A M , Guerra R , Cavaco A M , Pinto P , Andrade A , Duarte A , Power D M and Marques N T . 2017 . Identification of asymptomatic plants infected with Citrus tristeza virus from a time series of leaf spectral characteristics . Computers and Electronics in Agriculture , 141 : 340 - 350 [ DOI: 10.1016/j.compag.2017.08.001 http://dx.doi.org/10.1016/j.compag.2017.08.001 ]
Al-Saddik H , Simon J C and Cointault F . 2017 . Development of spectral disease indices for ‘Flavescence Dorée' grapevine disease identification . Sensors , 17 ( 12 ): 2772 [ DOI: 10.3390/s17122772 http://dx.doi.org/10.3390/s17122772 ]
Apan A , Held A , Phinn S and Markley J . 2004 . Detecting sugarcane ‘range rust’ disease using EO-1 Hyperion hyperspectral imagery . International Journal of Remote Sensing , 25 ( 2 ): 489 - 498 [ DOI: 10.1080/01431160310001618031 http://dx.doi.org/10.1080/01431160310001618031 ]
Bai Z G . 2018 . Retrospect and prospect of HJ-1A/B over the past ten years. Space International, (9): 4-6 (白照广 . 2018 . 环境减灾一号A、B卫星在轨运行十周年回顾与展望. 国际太空, (9): 4 - 6 )
Baranowski P , Jedryczka M , Mazurek W , Babula-Skowronska D , Siedliska A and Kaczmarek J . 2015 . Hyperspectral and thermal imaging of oilseed rape ( Brassica napus ) response to fungal species of the genus Alternaria . PLoS One , 10 ( 3 ): e 0122913 [ DOI: 10.1371/journal.pone.0122913 http://dx.doi.org/10.1371/journal.pone.0122913 ]
Bock C H , Poole G H , Parker P E and Gottwald T R . 2010 . Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging . Critical Reviews in Plant Sciences , 29 ( 2 ): 59 - 107 [ DOI: 10.1080/0735268100361 7285 http://dx.doi.org/10.1080/07352681003617285 ]
Bravo C , Moshou D , West J , McCartney A and Ramon H . 2003 . Early disease detection in wheat fields using spectral reflectance . Biosystems Engineering , 84 ( 2 ): 137 - 145 [ DOI: 10.1016/S1537-5110(02)00269-6 http://dx.doi.org/10.1016/S1537-5110(02)00269-6 ]
Cao X R , Luo Y , Zhou Y L , Fan J R , Xu X M , West J S , Duan X Y and Cheng D F . 2015 . Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance . PLoS One , 10 ( 3 ): e 0121462 [ DOI: 10.1371/journal.pone.0121462 http://dx.doi.org/10.1371/journal.pone.0121462 ]
Carvajal-Yepes M , Cardwell K , Nelson A , Garrett K A , Giovani B , Saunders D G O , Kamoun S , Legg J P , Verdier V , Lessel J , Neher R A , Day R , Pardey P , Gullino M L , Records A R , Bextine B , Leach J E , Staiger S and Tohme J . 2019 . A global surveillance system for crop diseases . Science , 364 ( 6447 ): 1237 - 1239 [ DOI: 10.1126/science.aaw1572 http://dx.doi.org/10.1126/science.aaw1572 ]
Ceccato P , Flasse S P , Tarantola S , Jacquemoud S P and Grégoire J M . 2001 . Detecting vegetation leaf water content using reflectance in the optical domain . Remote Sensing of Environment , 77 ( 1 ): 22 - 33 [ DOI: 10.1016/s0034-4257(01)00191-2 http://dx.doi.org/10.1016/s0034-4257(01)00191-2 ]
Chang C I . 2003 . Hyperspectral measures for spectral characterization . Springer US
Cheng F , Zhao Y R , Yu K Q , Lou B G , He Y . 2017 . Application of hyper-spectrafor detecting peroxidase content in cucumber leaves with early disease stress . Spectroscopy and Spectral Analysis , 37 ( 06 ): 1861 - 1865
程帆 , 赵艳茹 , 余克强 , 楼兵干 , 何勇 . 2017 . 基于高光谱技术的病害早期胁迫下黄瓜叶片中过氧化物酶活性的研究 . 光谱学与光谱分析 , 37 ( 06 ): 1861 - 1865 ) [ DOI: 10.3964/j.issn.1000-0593(201706-1861-05 http://dx.doi.org/10.3964/j.issn.1000-0593(2017)06-1861-05 ]
Cheng T , Rivard B , Sánchez-Azofeifa G A , Feng J and Calvo-Polanco M . 2010 . Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation . Remote Sensing of Environment , 114 ( 4 ): 899 - 910 [ DOI: 10.1016/j.rse.2009.12.005 http://dx.doi.org/10.1016/j.rse.2009.12.005 ]
Couture J J , Singh A , Charkowski A O , Groves R L , Gray S M , Bethke P C and Townsend P A . 2018 . Integrating spectroscopy with potato disease management . Plant Disease , 102 ( 11 ): 2233 - 2240 [ DOI: 10.1094/PDIS-01-18-0054-RE http://dx.doi.org/10.1094/PDIS-01-18-0054-RE ]
Delalieux S , van Aardt J , Keulemans W , Schrevens E and Coppin P . 2007 . Detection of biotic stress ( Venturia inaequalis ) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications . European Journal of Agronomy , 27 ( 1 ): 130 - 143 [ DOI: 10.1016/j.eja.2007.02.005 http://dx.doi.org/10.1016/j.eja.2007.02.005 ]
Devadas R , Lamb D W , Simpfendorfer S and Backhouse D . 2009 . Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves . Precision Agriculture , 10 ( 6 ): 459 - 470 [ DOI: 10.1007/s11119-008-9100-2 http://dx.doi.org/10.1007/s11119-008-9100-2 ]
Ding W J . 2019 . Hyperspectral remote sensing monitoring of scab of winter wheat based on different scales . Anhui University
丁文娟 . 2019 . 基于不同尺度的冬小麦赤霉病高光谱遥感监测 . 安徽大学
Fang S B , Han W and Pei Z F . 2020 . Desert Locust Swarms impact on the local vegetation along India-Pakistan border and their possible development trends . Journal of Remote Sensing , 24 ( 3 ): 326 - 332
房世波 , 韩威 , 裴志方 . 2020 . 沙漠蝗群对印巴边境植被的影响及其未来可能发展趋势 . 遥感学报 , 24 ( 3 ): 326 - 332 [ DOI: 10.11834/jrs.20200055 http://dx.doi.org/10.11834/jrs.20200055 ]
Gamon J A , Serrano L and Surfus J S . 1997 . The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels . Oecologia , 112 ( 4 ): 492 - 501 [ DOI: 10.1007/s004420050337 http://dx.doi.org/10.1007/s004420050337 ]
Gamon J A and Surfus J S . 1999 . Assessing leaf pigment content and activity with a reflectometer . New Phytologist , 143 ( 1 ): 105 - 117 [ DOI: 10.1046/j.1469-8137.1999.00424.x http://dx.doi.org/10.1046/j.1469-8137.1999.00424.x ]
Ghamisi P , Plaza J , Chen Y S , Li J and Plaza A J . 2017 . Advanced spectral classifiers for hyperspectral images: a review . IEEE Geoscience and Remote Sensing Magazine , 5 ( 1 ): 8 - 32 [ DOI: 10.1109/Mgrs.2016.2616418 http://dx.doi.org/10.1109/Mgrs.2016.2616418 ]
Gitelson A and Merzlyak M N . 1994 . Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L . and Acer platanoides L. Leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology , 143 ( 3 ): 286 - 292 [ DOI: 10.1016/S0176-1617(11)81633-0 http://dx.doi.org/10.1016/S0176-1617(11)81633-0 ]
Gitelson A A , Merzlyak M N and Chivkunova O B . 2010a . Optical properties and nondestructive estimation of anthocyanin content in plant leaves . Photochemistry and Photobiology , 74 ( 1 ): 38 - 45 [ DOI: 10.1562/0031-8655(2001)0740038opaneo2.0.co2 http://dx.doi.org/10.1562/0031-8655(2001)0740038opaneo2.0.co2 ]
Gitelson A , Zur Y , Chivkunova O B and Merzlyak M N . 2010b . Assessing carotenoid content in plant leaves with reflectance spectroscopy . Photochemistry and Photobiology , 75 ( 3 ): 272 - 281 [ DOI: 10.1562/0031-8655(2002)0750272accipl2.0.co2 http://dx.doi.org/10.1562/0031-8655(2002)0750272accipl2.0.co2 ]
Graeff S , Link J and Claupein W . 2006 . Identification of powdery mildew ( Erysiphe graminis sp . tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Open Life Sciences , 1 ( 2 ): 275 - 288 [ DOI: 10.2478/s11535-006-0020-8 http://dx.doi.org/10.2478/s11535-006-0020-8 ]
Grisham M P , Johnson R M and Zimba P V . 2010 . Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes . Journal of Virological Methods , 167 ( 2 ): 140 - 145 [ DOI: 10.1016/j.jviromet.2010.03.024 http://dx.doi.org/10.1016/j.jviromet.2010.03.024 ]
Gui J S , Wu Z X and Li K . 2019 . Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model . Journal of Zhejiang University (Agriculture and Life Sciences) , 45 ( 2 ): 256 - 262
桂江生 , 吴子娴 , 李凯 . 2019 . 基于卷积神经网络模型的大豆花叶病初期高光谱检测 . 浙江大学学报(农业与生命科学版) , 45 ( 2 ): 256 - 262 [ DOI: 10.3785/j.issn.1008-9209.2018.05.151 http://dx.doi.org/10.3785/j.issn.1008-9209.2018.05.151 ]
Haboudane D , Miller J R , Pattey E , Zarco-Tejada P J and Strachan I B . 2004 . Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture . Remote Sensing of Environment , 90 ( 3 ): 337 - 352 [ DOI: 10.1016/j.rse.2003.12.013 http://dx.doi.org/10.1016/j.rse.2003.12.013 ]
Han X Z . 2003 . Study on remote sensing mechanism and methods for East Asian migratory locust hazard monitoring . Institute of Remote Sensing Application Chinese Academy of Sciences
韩秀珍 . 2003 . 东亚飞蝗灾害的遥感监测机理与方法研究 [D]. 中国科学院研究生院(遥感应用研究所)
Hardisky M A , Klemas V and Smart R M . 1983 . The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of spartina alterniflora canopies . Photogrammetric Engineering and Remote Sensing , 49 ( 1 ): 77 - 83
Heim R H J , Wright I J , Allen A P , Geedicke I and Oldeland J . 2019 . Developing a spectral disease index for myrtle rust ( Austropuccinia psidii ) . Plant Pathology , 68 ( 4 ): 738 - 745 [ DOI: 10.1111/ppa.12996 http://dx.doi.org/10.1111/ppa.12996 ]
Huang L S , Zhao J L , Zhang D Y , Yuan L , Dong Y Y and Zhang J C . 2012 . Identifying and mapping stripe rust in winter wheat using multi-temporal airborne hyperspectral images . International Journal of Agriculture and Biology , 14 ( 5 ): 697 - 704
Huang W J , Lu J J , Ye H C , Kong W P , Mortimer A H and Shi Y . 2018 . Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis . International Journal of Agricultural and Biological Engineering , 11 ( 2 ): 145 - 152 [ DOI: 10.25165/j.ijabe.20181102.3467 http://dx.doi.org/10.25165/j.ijabe.20181102.3467 ]
Huang W J , Shi Y , Dong Y Y , Ye H C , Wu M Q , Cui B and Liu L Y . 2019 . Progress and prospects of crop diseases and pests monitoring by remote sensing . Smart Agriculture , 1 ( 4 ): 1 - 11
黄文江 , 师越 , 董莹莹 , 叶回春 , 邬明权 , 崔贝 , 刘林毅 . 2019 . 作物病虫害遥感监测研究进展与展望 . 智慧农业 , 1 ( 2 ): 1-11 [ DOI: 10.12133/j.smartag.2019.1.4.201905-SA005 http://dx.doi.org/10.12133/j.smartag.2019.1.4.201905-SA005 ]
Jia S P , Gao H J and Hang X . 2019 . Research progress on image recognition technology of crop pests and diseases based on deep learning . Transactions of the Chinese Society for Agricultural Machinery , 50 ( S ): 313 - 317
贾少鹏 , 高红菊 , 杭萧 . 2019 . 基于深度学习的农作物病虫害图像识别技术研究进展 . 农业机械学报 , 50 ( 增刊 ): 313 - 317 [ DOI: 10.6041/j.issn.1000-1298.2019.S0.048 http://dx.doi.org/10.6041/j.issn.1000-1298.2019.S0.048 ]
Jin X , Jie L , Wang S , Qi H J and Li S W . 2018 . Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field . Remote Sensing , 10 ( 3 ): 395 [ DOI: 10.3390/rs10030395 http://dx.doi.org/10.3390/rs10030395 ]
Joalland S , Screpanti C , Liebisch F , Varella H V , Gaume A and Walter A . 2017 . Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets . Plant Methods , 13 : 73 [ DOI: 10.1186/s13007-017-0223-1 http://dx.doi.org/10.1186/s13007-017-0223-1 ]
Knauer U , Matros A , Petrovic T , Zanker T , Scott E S and Seiffert U . 2017 . Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images . Plant Methods , 13 : 47 [ DOI: 10.1186/s13007-017-0198-y http://dx.doi.org/10.1186/s13007-017-0198-y ]
Kong W W , Liu F , Zhang C , Bao Y D , Yu J J and He Y . 2014 . Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging . Spectrochimica Acta Part A : Molecular and Biomolecular Spectroscopy , 118 : 498 - 502 [ DOI: 10.1016/j.saa.2013.09.009 http://dx.doi.org/10.1016/j.saa.2013.09.009 ]
Li G X , Wang C , Feng M C , Yang W D , Li F Z and Feng R Y . 2017 . Hyperspectral prediction of leaf area index of winter wheat in irrigated and rainfed fields . PLoS One , 12 ( 8 ): e 0183338 [ DOI: 10.1371/journal.pone.0183338 http://dx.doi.org/10.1371/journal.pone.0183338 ]
Li Y T . 2016 . Immigration Atmospheric Backgrounds and Remoting Sensing Monitoring of Cnaphalocris medinalis Guenee in Central Jiangsu . Nanjing : Nanjing University of Information Science and Technology
李玉婷 . 2016 . 苏中稻区稻纵卷叶螟迁入的大气背景和遥感监测 . 南京 : 南京信息工程大学
Li W . 2019 . Research progress on application of hyperspectral remote sensing technology in pest and disease monitoring . Modern Agricultural Science and Technology , ( 14 ): 126 - 128
李玮 . 2019 . 农业病虫害监测中高光谱遥感技术应用研究进展. 现代农业科技, (14): 126 - 128
Li W G , Huang W J , Dong Y Y , Chen H , Wang J J and Shan J . 2017 . Estimation on winter wheat scab based on combination of temperature, humidity and remote sensing vegetation index . Transactions of the Chinese Society of Agricultural Engineering , 33 ( 23 ): 203 - 210
李卫国 , 黄文江 , 董莹莹 , 陈华 , 王晶晶 , 单婕 . 2017 . 基于温湿度与遥感植被指数的冬小麦赤霉病估测 . 农业工程学报 , 33 ( 23 ): 203 - 210 [ DOI: 10.11975/j.issn.1002-6819.2017.23.026 http://dx.doi.org/10.11975/j.issn.1002-6819.2017.23.026 ]
Li X H , Lee W S , Li M Z , Ehsani R , Mishra A R , Yang C H and Mangan R L . 2012 . Spectral difference analysis and airborne imaging classification for citrus greening infected trees . Computers and Electronics in Agriculture , 83 : 32 - 46 [ DOI: 10.1016/j.compag.2012.01.010 http://dx.doi.org/10.1016/j.compag.2012.01.010 ]
Liang K , Du Y Y , Lu W , Wang C , Xu J H and Shen M X . 2016 . Identification of Fusarium head blight wheat based on hyperspectral imaging technology . Transactions of the Chinese Society for Agricultural Machinery , 47 ( 2 ): 309 - 315
梁琨 , 杜莹莹 , 卢伟 , 王策 , 徐剑宏 , 沈明霞 . 2016 . 基于高光谱成像技术的小麦籽粒赤霉病识别 . 农业机械学报 , 47 ( 2 ): 309 - 315 [ DOI: 10.6041/j.issn.1000-1298.2016.02.041 http://dx.doi.org/10.6041/j.issn.1000-1298.2016.02.041 ]
Liu L Y , Huang M Y , Huang W J , Wang J H , Zhao C J , Zheng L F and Tong Q X . 2004 . Monitoring stripe rust disease of winter wheat using multi-temporal hyperspectral airborne data . Journal of Remote Sensing , 8 ( 3 ): 275 - 281
刘良云 , 黄木易 , 黄文江 , 王纪华 , 赵春江 , 郑兰芬 , 童庆禧 . 2004 . 利用多时相的高光谱航空图像监测冬小麦条锈病 . 遥感学报 , 8 ( 3 ): 275 - 281 [ DOI: 10.3321/j.issn:1007-4619.2004.03.013 http://dx.doi.org/10.3321/j.issn:1007-4619.2004.03.013 ]
Liu P , Zhang J C , Yang P T , Wang B T and Wu K H . 2017 . Analysis on monitoring of wheat stripe rust at multiple stages and optimization of bands for disease detection . Chinese Journal of Biological Control , 33 ( 6 ): 833 - 841
刘鹏 , 张竞成 , 杨娉婷 , 王保通 , 吴开华 . 2017 . 多生育期小麦条锈病光谱波段优选及监测研究 . 中国生物防治学报 , 33 ( 6 ): 833 - 841 [ DOI: 10.16409/j.cnki.2095-039x.2017.06.017 http://dx.doi.org/10.16409/j.cnki.2095-039x.2017.06.017 ]
Liu Q , Wang C C , Wang R , Gu Y L , Li W and Ma Z H . 2018 . Hyperspectral qualitative identification on latent period of wheat stripe rust . Journal of Plant Protection , 45 ( 1 ): 153 - 160
刘琦 , 王翠翠 , 王睿 , 谷医林 , 李薇 , 马占鸿 . 2018 . 潜育期小麦条锈菌的高光谱定性识别 . 植物保护学报 , 45 ( 1 ): 153 - 160 [ DOI: 10.13802/j.cnki.zwbhxb.2018.2018916 http://dx.doi.org/10.13802/j.cnki.zwbhxb.2018.2018916 ]
Liu T Y , Feng Q and Yang S . 2018 . Detecting grape diseases based on Convolutional neural network . Journal of Northeast Agricultural University , 49 ( 3 ): 73 - 83
刘阗宇 , 冯全 , 杨森 . 2018 . 基于卷积神经网络的葡萄叶片病害检测方法 . 东北农业大学学报 , 49 ( 3 ): 73-83 [ DOI: 10.19720/j.cnki.issn.1005-9369.2018.03.009 http://dx.doi.org/10.19720/j.cnki.issn.1005-9369.2018.03.009 ]
Liu Z Y , Qi J G , Wang N N , Zhu Z R , Luo J , Liu L J , Tang J and Cheng J A . 2018 . Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network . Precision Agriculture , 19 ( 6 ): 973 - 991 [ DOI: 10.1007/s11119-018-9567-4 http://dx.doi.org/10.1007/s11119-018-9567-4 ]
López-López M , Calderón R , González-Dugo V , Zarco-Tejada P and Fereres E . 2016 . Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery . Remote Sensing , 8 ( 4 ): 276 [ DOI: 10.3390/rs8040276 http://dx.doi.org/10.3390/rs8040276 ]
Lowe A , Harrison N and French A P . 2017 . Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress . Plant Methods , 13 : 80 [ DOI: 10.1186/s13007-017-0233-z http://dx.doi.org/10.1186/s13007-017-0233-z ]
Lu J Z , Zhou M C , Gao Y W and Jiang H Y . 2018 . Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves . Precision Agriculture , 19 ( 3 ): 379 - 394 [ DOI: 10.1007/s11119-017-9524-7 http://dx.doi.org/10.1007/s11119-017-9524-7 ]
Luo X , Hong T S , Luo K , Dai F , Wu W B , Mei H X and Lin L . 2016 . Application of wavelet transform and successive projections algorithm in the non-destructive measurement of total acid content of pitaya . Spectroscopy and Spectral Analysis , 36 ( 5 ): 1345 - 1351
罗霞 , 洪添胜 , 罗阔 , 代芬 , 吴伟斌 , 梅慧兰 , 林凛 . 2016 . 小波变换和连续投影算法在火龙果总酸无损检测中的应用 . 光谱学与光谱分析 , 36 ( 5 ): 1345 - 1351 ) [ DOI: 10.3964/j.issn.1000-0593(201605-1345-07 http://dx.doi.org/10.3964/j.issn.1000-0593(2016)05-1345-07 ]
Ma H Q , Jing Y S , Huang W J , Shi Y , Dong Y Y , Zhang J C and Liu L Y . 2018 . Integrating early growth information to monitor winter wheat powdery mildew using multi-temporal landsat-8 imagery . Sensors , 18 ( 10 ): 3290 [ DOI: 10.3390/s18103290 http://dx.doi.org/10.3390/s18103290 ]
Mahlein A K , Alisaac E , Al Masri A , Behmann J , Dehne H W and Oerke E C . 2019 . Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale . Sensors , 19 ( 10 ): 2281 [ DOI: 10.3390/s19102281 http://dx.doi.org/10.3390/s19102281 ]
Mahlein A K , Kuska M T , Behmann J , Polder G and Walter A . 2018 . Hyperspectral sensors and imaging technologies in phytopathology: state of the art . Annual Review of Phytopathology , 56 ( 1 ): 535 - 558 [ DOI: 10.1146/annurev-phyto-080417-050100 http://dx.doi.org/10.1146/annurev-phyto-080417-050100 ]
Mahlein A K , Rumpf T , Welke P , Oerke E C , Plümer L , Steiner U and Oerke E C . 2013 . Development of spectral indices for detecting and identifying plant diseases . Remote Sensing of Environment , 128 : 21 - 30 [ DOI: 10.1016/j.rse.2012.09.019 http://dx.doi.org/10.1016/j.rse.2012.09.019 ]
Mahlein A K , Steiner U , Hillnhütter C , Dehne H W and Oerke E C . 2012 . Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases . Plant Methods , 8 : 3 [ DOI: 10.1186/1746-4811-8-3 http://dx.doi.org/10.1186/1746-4811-8-3 ]
Martinelli F , Scalenghe R , Davino S , Panno S , Scuderi G , Ruisi P , Villa P , Stroppiana D , Boschetti M , Goulart L R , Davis C E and Dandekar A M . 2015 . Advanced methods of plant disease detection . A review. Agronomy for Sustainable Development , 35 ( 1 ): 1 - 25 [ DOI: 10.1007/s13593-014-0246-1 http://dx.doi.org/10.1007/s13593-014-0246-1 ]
Mirik M , Jones D C , Price J A , Workneh F , Ansley R J and Rush C M . 2011 . Satellite remote sensing of wheat infected by Wheat streak mosaic virus . Plant Disease , 95 ( 1 ): 4 - 12 [ DOI: 10.1094/PDIS-04-10-0256 http://dx.doi.org/10.1094/PDIS-04-10-0256 ]
Mirik M , Michels G J , Kassymzhanova-Mirik S , Elliott N C , Catana V , Jones D B and Bowling R . 2006 . Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat. Computers and Electronics in Agriculture, 51( 1 / 2 ): 86 - 98 [ DOI: 10.1016/j.compag.2005.11.004 http://dx.doi.org/10.1016/j.compag.2005.11.004 ]
Moshou D , Bravo C , Oberti R , West J , Bodria L , McCartney A and Ramon H . 2005 . Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps . Real-Time Imaging , 11 ( 2 ): 75 - 83 [ DOI: 10.1016/j.rti.2005.03.003 http://dx.doi.org/10.1016/j.rti.2005.03.003 ]
Moshou D , Bravo C , Oberti R , West J S , Ramon H , Vougioukas S and Bochtis D . 2011 . Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops . Biosystems Engineering , 108 ( 4 ): 311 - 321 [ DOI: 10.1016/j.biosystemseng.2011.01.003 http://dx.doi.org/10.1016/j.biosystemseng.2011.01.003 ]
Moshou D , Bravo C , West J , Wahlen S , McCartney A and Ramon H . 2004 . Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks . Computers and Electronics in Agriculture , 44 ( 3 ): 173 - 188 [ DOI: 10.1016/j.compag.2004.04.003 http://dx.doi.org/10.1016/j.compag.2004.04.003 ]
Nagasubramanian K , Jones S , Sarkar S , Singh A K , Singh A and Ganapathysubramanian B . 2018 . Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems . Plant Methods , 14 : 86 [ DOI: 10.1186/s13007-018-0349-9 http://dx.doi.org/10.1186/s13007-018-0349-9 ]
Nie C W , Yuan L , Wang B T , Jin X L , Huang W J , Zhang J C and Yang G J . 2016 . Monitoring wheat powdery mildew based on integrated remote sensing and meteorological information . Acta Phytopathologica Sinica , 46 ( 2 ): 285 - 288
聂臣巍 , 袁琳 , 王保通 , 金秀良 , 黄文江 , 张竞成 , 杨贵军 . 2016 . 综合遥感与气象信息的小麦白粉病监测方法 . 植物病理学报 , 46 ( 2 ): 285 - 288 [ DOI: 10.13926/j.cnki.apps.2016.02.018 http://dx.doi.org/10.13926/j.cnki.apps.2016.02.018 ]
Oerke E C , Herzog K and Toepfer R . 2016 . Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola . Journal of Experimental Botany , 67 ( 18 ): 5529 - 5543 [ DOI: 10.1093/jxb/erw318 http://dx.doi.org/10.1093/jxb/erw318 ]
Pei P C . 2019 . Study on Monitoring of Cotton Aphid Damage Level Based on UAV Hyperspectral Images . Zhengzhou : Henan Agricultural University
裴鹏程 . 2019 . 利用无人机成像高光谱监测棉蚜为害等级研究 . 郑州 : 河南农业大学
Peñuelas J , Baret F and Filella I . 1995 . Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance . Photosynthetica , 31 ( 2 ): 221 - 230
Perez-Sanz F , Navarro P J and Egea-Cortines M . 2017 . Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms . GigaScience , 6 ( 11 ):
gix 092 [ DOI: 10.1093/gigascience/gix092 http://dx.doi.org/10.1093/gigascience/gix092 ]
Polder G , Blok P M , de Villiers H A C , van der Wolf J M and Kamp J . 2019 . Potato virus Y detection in seed potatoes using deep learning on hyperspectral images . Frontiers in Plant Science , 10 : 209 [ DOI: 10.3389/fpls.2019.00209 http://dx.doi.org/10.3389/fpls.2019.00209 ]
Prabhakar M , Prasad Y G , Thirupathi M , Sreedevi G , Dharajothi B and Venkateswarlu B . 2011 . Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae) . Computers and Electronics in Agriculture , 79 ( 2 ): 189 - 198 [ DOI: 10.1016/j.compag.2011.09.012 http://dx.doi.org/10.1016/j.compag.2011.09.012 ]
Qiao H B , Zhou Y L , Bai Y L , Cheng D F and Duan X Y . 2006 . The primary research of detecting wheat powdery mildew using in-field and low altitude remote sensing . Acta Phytophylacica Sinica , 33 ( 4 ): 341 - 344
乔红波 , 周益林 , 白由路 , 程登发 , 段霞瑜 . 2006 . 地面高光谱和低空遥感监测小麦白粉病初探 . 植物保护学报 , 33 ( 4 ): 341 - 344 [ DOI: 10.3321/j.issn:0577-7518.2006.04.002 http://dx.doi.org/10.3321/j.issn:0577-7518.2006.04.002 ]
Qin J W , Burks T F , Ritenour M A and Bonn W G . 2009 . Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence . Journal of Food Engineering , 93 ( 2 ): 183 - 191 [ DOI: 10.1016/j.jfoodeng.2009.01.014 http://dx.doi.org/10.1016/j.jfoodeng.2009.01.014 ]
Rouse J W , Haas R H , Schell J A and Deering D W . 1973 . Monitoring vegetation systems in the great plains with ERTS // Proceedings of the Third ERTS Symposium . Washington DC : Nasa Special Publication , 351 : 309 - 317
Rumpf T , Mahlein A K , Steiner U , Oerke E C , Dehne H W and Plümer L . 2010 . Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance . Computers and Electronics in Agriculture , 74 ( 1 ): 91 - 99 [ DOI: 10.1016/j.compag.2010.06.009 http://dx.doi.org/10.1016/j.compag.2010.06.009 ]
Sankaran S , Ehsani R , Inch S A and Ploetz R C . 2012 . Evaluation of visible-near infrared reflectance spectra of avocado leaves as a non-destructive sensing tool for detection of laurel wilt . Plant Disease , 96 ( 11 ): 1683 - 1689 [ DOI: 10.1094/Pdis-01-12-0030-Re http://dx.doi.org/10.1094/Pdis-01-12-0030-Re ]
Schaepman M E , Ustin S L , Plaza A J , Painter T H , Verrelst J and Liang S L . 2009 . Earth system science related imaging spectroscopy-An assessment. Remote Sensing of Environment, 113 ( S 1 ): S123 - S137 [ DOI: 10.1016/J.RSE.2009.03.001 http://dx.doi.org/10.1016/J.RSE.2009.03.001 ]
Serrano L , Penuelas J and Ustin S L . 2002 . Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment, 81( 2 / 3 ): 355 - 364 [ DOI: 10.1016/S0034-4257(02)00011-1 http://dx.doi.org/10.1016/S0034-4257(02)00011-1 ]
Shahin M A and Symons S J . 2011 . Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis . Computers and Electronics in Agriculture , 75 ( 1 ): 107 - 112 [ DOI: 10.1016/j.compag.2010.10.004 http://dx.doi.org/10.1016/j.compag.2010.10.004 ]
Shi Y , Huang W J , Luo J H , Huang L S and Zhou X F . 2017 . Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis . Computers and Electronics in Agriculture , 141 : 171 - 180 [ DOI: 10.1016/j.compag.2017.07.019 http://dx.doi.org/10.1016/j.compag.2017.07.019 ]
Shi Y , Huang W J , González-Moreno P , Luke B , Dong Y Y , Zheng Q , Ma H Q and Liu L Y . 2018 . Wavelet-based rust spectral feature set (WRSFs): a novel spectral feature set based on continuous wavelet transformation for tracking progressive host-pathogen interaction of yellow rust on wheat . Remote Sensing , 10 ( 4 ): 525 [ DOI: 10.3390/rs10040525 http://dx.doi.org/10.3390/rs10040525 ]
Singh C B , Jayas D S , Paliwal J , White N D G . 2010 . Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging . Biosystems Engineering , 105 , 380 - 387 [ DOI: 10.1016/j.biosystemseng.2009.12.009 http://dx.doi.org/10.1016/j.biosystemseng.2009.12.009 ]
Sun J , Tan W J , Mao H P , Wu X H , Chen Y and Wang L . 2017 . Recognition of multiple plant leaf diseases based on improved convolutional neural network . Transactions of the Chinese Society of Agricultural Engineering , 33 ( 19 ): 209 - 215
孙俊 , 谭文军 , 毛罕平 , 武小红 , 陈勇 , 汪龙 . 2017 . 基于改进卷积神经网络的多种植物叶片病害识别 . 农业工程学报 , 33 ( 19 ): 209 - 215 [ DOI: 10.11975/j.issn.1002-6819.2017.19.027 http://dx.doi.org/10.11975/j.issn.1002-6819.2017.19.027 ]
Susič N , Žibrat U , Širca S , Strajnar P , Razinger J , Knapič M , Vončina A , Urek G and Gerič Stare B . 2018 . Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging . Sensors and Actuators B : Chemical , 273 : 842 - 852 [ DOI: 10.1016/j.snb.2018.06.121 http://dx.doi.org/10.1016/j.snb.2018.06.121 ]
Tang X L , Du Y M , Liu Y W , Li J X and Ma Y W . 2018 . Image recognition with conditional deep convolutional generative adversarial networks . Acta Automatica Sinica , 44 ( 5 ): 855 - 864
唐贤伦 , 杜一铭 , 刘雨微 , 李佳歆 , 马艺玮 . 2018 . 基于条件深度卷积生成对抗网络的图像识别方法 . 自动化学报 , 44 ( 5 ): 855 - 864 [ DOI: 10.16383/j.aas.2018.c170470 http://dx.doi.org/10.16383/j.aas.2018.c170470 ]
Tekle S , Måge I , Segtnan V H and Bjørnstad Å . 2015 . Near-infrared hyperspectral imaging of Fusarium -damaged oats ( Avena sativa L . ). Cereal Chemistry , 92 ( 1 ): 73 - 80 [ DOI: 10.1094/CCHEM-04-14-0074-R http://dx.doi.org/10.1094/CCHEM-04-14-0074-R ]
Thomas S , Kuska M T , Bohnenkamp D , Brugger A , Alisaac E , Wahabzada M , Behmann J and Mahlein A K . 2018 . Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective . Journal of Plant Diseases and Protection , 125 ( 1 ): 5 - 20 [ DOI: 10.1007/s41348-017-0124-6 http://dx.doi.org/10.1007/s41348-017-0124-6 ]
Tong Q X , Zhang B and Zhang L F . 2016 . Current progress of hyperspectral remote sensing in China . Journal of Remote Sensing , 20 ( 5 ): 689 - 707
童庆禧 , 张兵 , 张立福 . 2016 . 中国高光谱遥感的前沿进展 . 遥感学报 , 20 ( 5 ): 689 - 707 [ DOI: 10.11834/jrs.2016 6264 http://dx.doi.org/10.11834/jrs.20166264 ]
Vanegas F , Bratanov D , Powell K , Weiss J and Gonzalez F . 2018 . A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data . Sensors , 18 ( 1 ): 260 [ DOI: 10.3390/s18010260 http://dx.doi.org/10.3390/s18010260 ]
Wahabzada M , Mahlein A K , Bauckhage C , Steiner U , Oerke E C and Kersting K . 2016 . Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants . Scientific Reports , 6 : 22482 [ DOI: 10.1038/srep22482 http://dx.doi.org/10.1038/srep22482 ]
Wang D and Chai X J . 2019 . Application of machine learning in plant diseases recognition . Journal of Chinese Agricultural Mechanization , 40 ( 9 ): 171 - 180
王聃 , 柴秀娟 . 2019 . 机器学习在植物病害识别研究中的应用 . 中国农机化学报 , 40 ( 9 ): 171 - 180 [ DOI: 10.13733/j.jcam.issn.2095-5553.2019.09.30 http://dx.doi.org/10.13733/j.jcam.issn.2095-5553.2019.09.30 ]
Wang F , Li Y Y , Peng Y K , Yang B N , Li L and Liu Y C . 2018 . Multi-parameter potato quality non-destructive rapid detection by visible/near-infrared spectra . Spectroscopy and Spectral Analysis , 38 ( 12 ): 3736 - 3742
王凡 , 李永玉 , 彭彦昆 , 杨炳南 , 李龙 , 刘亚超 . 2018 . 马铃薯多品质参数可见/近红外光谱无损快速检测 . 光谱学与光谱分析 , 38 ( 12 ): 3736 - 3742 ) [ DOI: 10.3964/j.issn.1000-0593(201812-3736-07 http://dx.doi.org/10.3964/j.issn.1000-0593(2018)12-3736-07 ]
Wang J T , Wu Y L , Liao Y and Chen Y Y . 2020 . Hyperspectral classification of Citrus Leaves Based on convolution neural network . Information Technology and Informatization , ( 3 ): 84 - 87
王建涛 , 吴叶兰 , 廖禺 , 陈怡宇 . 2020 . 基于卷积神经网络的柑橘病叶高光谱分类. 信息技术与信息化, (3): 84 - 87 [DOI: 10.3969/j.issn.1672-9528.2020.03.030]
Wang X F , Zhang C L , Zhang S W and Zhu Y H . 2018 . Forecasting of cotton diseases and pests based on adaptive discriminant deep belief network . Transactions of the Chinese Society of Agricultural Engineering , 34 ( 14 ): 157 - 164
王献锋 , 张传雷 , 张善文 , 朱义海 . 2018 . 基于自适应判别深度置信网络的棉花病虫害预测 . 农业工程学报 , 34 ( 14 ): 157 - 164 [ DOI: 10.11975/j.issn.1002-6819.2018.14.020 http://dx.doi.org/10.11975/j.issn.1002-6819.2018.14.020 ]
Wang Y D . 2016 . Research on hyperspectral characteristics of tobacco mosaic virus disease and discriminant analysis model of disease degree . Henan Agricultural University
王一丁 . 2016 . 烟草花叶病害高光谱特征及其病害程度判别分析模型的研究 . 河南农业大学
Xie C Q , Yang C and He Y . 2017 . Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities . Computers and Electronics in Agriculture , 135 : 154 - 162 [ DOI: 10.1016/j.compag.2016.12.015 http://dx.doi.org/10.1016/j.compag.2016.12.015 ]
Xing X Q . 2016 . Research on corn borer detection method based on hyperspectral imaging technology . Agricultural Electrification And Automation
邢晓祺 . 2016 . 基于高光谱成像技术的玉米螟检测方法研究 . 沈阳农业大学
Yang S L , Yang X B and Mo J Y . 2018 . The application of unmanned aircraft systems to plant protection in China . Precision Agriculture , 19 ( 2 ): 278 - 292 [ DOI: 10.1007/s11119-017-9516-7 http://dx.doi.org/10.1007/s11119-017-9516-7 ]
Yao Y J , Qin Q M , Zhang Z L and Li B S . 2008 . Research progress of hyperspectral technology applied in agricultural remote sensing . Transactions of the CSAE , 24 ( 7 ): 301 - 306
姚云军 , 秦其明 , 张自力 , 李百寿 . 2008 . 高光谱技术在农业遥感中的应用研究进展 . 农业工程学报 , 24 ( 7 ): 301 - 306 [ DOI: 10.3321/j.issn:1002-6819.2008.07.063 http://dx.doi.org/10.3321/j.issn:1002-6819.2008.07.063 ]
Yao Z F , Lei Y and He D J . 2019 . Identification of powdery mildew and stripe rust in wheat using hyperspectral imaging . Spectroscopy and Spectral Analysis , 39 ( 3 ): 969 - 976
姚志凤 , 雷雨 , 何东健 . 2019 . 基于高光谱成像的小麦白粉病与条锈病识别 . 光谱学与光谱分析 , 39 ( 3 ): 969 - 976 ) [ DOI: 10.3964/j.issn.1000-0593(201903-0969-08 http://dx.doi.org/10.3964/j.issn.1000-0593(2019)03-0969-08 ]
Yeh Y H F , Chung W C , Liao J Y , Chung C L , Kuo Y F and Lin T T . 2013 . A comparison of machine learning methods on hyperspectral plant disease assessments . IFAC Proceedings Volumes , 46 ( 4 ): 361 - 365 [ DOI: 10.3182/20130327-3-jp-3017.00081 http://dx.doi.org/10.3182/20130327-3-jp-3017.00081 ]
Yuan L . 2015 . Identification and Differentiation of Wheat Diseases and Insects with Multi-source and Multi-scale Remote Sensing Data . Hangzhou : Zhejiang University
袁琳 . 2015 . 小麦病虫害多尺度遥感识别和区分方法研究 . 杭州 : 浙江大学
Yuan L , Huang Y B , Loraamm R W , Nie C W , Wang J H and Zhang J C . 2014 . Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects . Field Crops Research , 156 : 199 - 207 [ DOI: 10.1016/j.fcr.2013.11.012 http://dx.doi.org/10.1016/j.fcr.2013.11.012 ]
Zhang C Y , Chen W D and Sankaran S . 2019b . High-throughput field phenotyping of Ascochyta blight disease severity in chickpea . Crop Protection , 125 : 104885 [ DOI: 10.1016/j.cropro.2019.104885 http://dx.doi.org/10.1016/j.cropro.2019.104885 ]
Zhang J C , Huang Y B , Pu R L , Gonzalez-Moreno P , Yuan L , Wu K H and Huang W J . 2019a . Monitoring plant diseases and pests through remote sensing technology: a review . Computers and Electronics in Agriculture , 165 : 104943 [ DOI: 10.1016/j.compag.2019.104943 http://dx.doi.org/10.1016/j.compag.2019.104943 ]
Zhang J C , Yuan L , Wang J H , Luo J H , Du S Z and Huang W J . 2012 . Research progress of crop diseases and pests monitoring based on remote sensing . Transactions of the Chinese Society of Agricultural Engineering , 28 ( 20 ): 1 - 11
张竞成 , 袁琳 , 王纪华 , 罗菊花 , 杜世州 , 黄文江 . 2012 . 作物病虫害遥感监测研究进展 . 农业工程学报 , 28 ( 20 ): 1-11 [ DOI: 10.3969/j.issn.1002-6819.2012.20.001 http://dx.doi.org/10.3969/j.issn.1002-6819.2012.20.001 ]
Zhang J K , Rivard B and Rogge D M . 2008 . The successive projection algorithm (SPA), an algorithm with a spatial constraint for the automatic search of endmembers in hyperspectral data . Sensors , 8 ( 2 ): 1321 - 1342 [ DOI: 10.3390/s8021321 http://dx.doi.org/10.3390/s8021321 ]
Zhang M H , Qin Z H , Liu X and Ustin S L . 2003 . Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing . International Journal of Applied Earth Observation and Geoinformation , 4 ( 4 ): 295 - 310 [ DOI: 10.1016/s0303-2434(03)00008-4 http://dx.doi.org/10.1016/s0303-2434(03)00008-4 ]
Zhang N , Pan Y C , Feng H K , Zhao X Q , Yang X D , Ding C L and Yang G J . 2019c . Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets . Biosystems Engineering , 186 : 83 - 99 [ DOI: 10.1016/j.biosystemseng.2019.06.008 http://dx.doi.org/10.1016/j.biosystemseng.2019.06.008 ]
Zhou X , Bao Y X , Wang L , Du Z Z , Tang Q and Chen C . 2020 . Hyperspectral features of rice canopy and SPAD values estimation under the stress of rice leaf folder . Chinese Journal of Agrometeorology , 41 ( 3 ): 173 - 186
周晓 , 包云轩 , 王琳 , 杜正朕 , 唐倩 , 陈粲 . 2020 . 稻纵卷叶螟为害水稻的冠层光谱特征及叶绿素含量估算 . 中国农业气象 , 41 ( 3 ): 173 - 186 [ DOI: 10.3969/j.issn.1000-6362.2020.03.006 http://dx.doi.org/10.3969/j.issn.1000-6362.2020.03.006 ]
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