无人机遥感监测作物病虫害胁迫方法与最新研究进展

doi:10.12133/j.smartag.SA202201008

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 1-16.doi: 10.12133/j.smartag.SA202201008

• 专题--作物生长及其环境监测 • 下一篇

无人机遥感监测作物病虫害胁迫方法与最新研究进展

杨国峰^1,^2,³(), 何勇^1,^2,³(), 冯旭萍^1,^2,³, 李禧尧^1,^2,³, 张金诺^1,^2,³, 俞泽宇^1,^2,³

^1.浙江大学华南工业技术研究院，广东广州 510700
^2.浙江大学生物系统工程与食品科学学院，浙江杭州 310058
^3.农业农村部光谱检测重点实验室，浙江杭州 310058

收稿日期:2022-01-26 出版日期:2022-03-30
基金资助:
广东省重点领域研发计划项目(2019B020216001);国家自然科学基金(31801257)
作者简介:杨国峰（1994－），男，博士研究生，研究方向为农业计算机视觉与无人机植物表型。E-mail：yangguofeng@zju.edu.cn。
通信作者: 何勇（1963－），男，博士，教授，研究方向为数字农业与精细农业、农业物联网技术、农业机械装备智能化检测与节能、管理信息系统与农用航空等。E-mail：yhe@zju.edu.cn

Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle

YANG Guofeng^1,^2,³(), HE Yong^1,^2,³(), FENG Xuping^1,^2,³, LI Xiyao^1,^2,³, ZHANG Jinnuo^1,^2,³, YU Zeyu^1,^2,³

^1.Huanan Industrial Technology Research Institute of Zhejiang University, Guangzhou 510700, China
^2.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
^3.The Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China

Received:2022-01-26 Online:2022-03-30
corresponding author: HE Yong, E-mail：yhe@zju.edu.cn
About author:YANG Guofeng, E-mail：yangguofeng@zju.edu.cn
Supported by:
Guangdong Key Areas Research and Development Project (2019B020216001); National Natural Science Foundation of China (31801257)

摘要/Abstract

摘要：

病虫害是作物生产面临的主要胁迫之一。近年来，随着无人机产业的快速发展，无人机农业遥感因其图像空间分辨率高、数据获取时效性强和成本低等特点，在作物病虫害胁迫监测应用中发挥了重要作用。本文首先介绍了利用无人机遥感监测作物病虫害胁迫的相关背景；其次对目前无人机遥感监测作物病虫害胁迫中的常用方法进行了概述，主要探讨无人机遥感监测作物病虫害胁迫的数据获取方式和数据处理方法；之后从可见光成像遥感、多光谱成像遥感、高光谱成像遥感、热红外成像遥感、激光雷达成像遥感和多遥感融合与对比六个方面重点综述了近期国内外无人机遥感监测作物病虫害胁迫的研究进展。最后提出了无人机遥感监测作物病虫害胁迫研究与应用中尚未解决的关键技术问题与未来的发展方向。本文为把握无人机遥感监测作物病虫害胁迫研究热点、应用瓶颈、发展趋势提供借鉴和参考，以期助力中国无人机遥感监测作物病虫害胁迫更加标准化、信息化、精准化和智能化。

关键词: 无人机, 遥感监测, 病虫害胁迫, 数据获取, 数据处理, 深度学习, 多遥感融合

Abstract:

Diseases and pests are main stresses to crop production. It is necessary to accurately and quickly monitor and control the stresses dynamically, so as to ensure the food security and the quality and safety of agricultural products, protect the ecological environment, and promote the sustainable development of agriculture. In recent years, with the rapid development of the unmanned aerial vehicle (UAV) industry, UAV agricultural remote sensing has played an important role in the application of crop diseases and pests monitoring due to its high image spatial resolution, strong data acquisition timeliness and low cost. The relevant background of UAV remote sensing monitoring of crop disease and pest stress was introduced, then the current methods commonly used in remote sensing monitoring of crop disease and pest stress by UAV was summarized. The data acquisition method and data processing method of UAV remote sensing monitoring of crop disease and pest stress were mainly discussed. Then, from the six aspects of visible light imaging remote sensing, multispectral imaging remote sensing, hyperspectral imaging remote sensing, thermal infrared imaging remote sensing, LiDAR imaging remote sensing and multiple remote sensing fusion and comparison, the research progress of remote sensing monitoring of crop diseases and pests by UAV worldwide was reviewed. Finally, the unresolved key technical problems and future development directions in the research and application of UAV remote sensing monitoring of crop disease and pest stress were proposed. Such as, the performance of the UAV flight platform needs to be optimized and upgraded, as well as the development of low-cost, lightweight, modular, and more adaptable airborne sensors. Convenient and automated remote sensing monitoring tasks need to be designed and implemented, and more remote sensing monitoring information can be obtained. Data processing algorithms or software should be designed and developed with greater applicability and wider applicability, and data processing time should be shortened by using 5G-based communication networks and edge computing devices. The applicability of the algorithm or model for UAV remote sensing monitoring of crop disease and pest stress needs to be stronger, so as to build a corresponding method library. We hope that this paper can help Chinese UAV remote sensing monitoring of crop diseases and pests to achieve more standardization, informatization, precision and intelligence.

Key words: unmanned aerial vehicle, remote sensing monitoring, diseases and pests stress, data acquisition, data processing, deep learning, multiple remote sensing fusion

中图分类号:

S-435

杨国峰, 何勇, 冯旭萍, 李禧尧, 张金诺, 俞泽宇. 无人机遥感监测作物病虫害胁迫方法与最新研究进展[J]. 智慧农业(中英文), 2022, 4(1): 1-16.

YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu. Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle[J]. Smart Agriculture, 2022, 4(1): 1-16.

参考文献

1	CARVAJAL-YEPES M, CARDWELL K, NELSON A, et al. A global surveillance system for crop diseases[J]. Science, 2019, 364(6447): 1237-1239.
2	WANG C, WANG X, JIN Z, et al. Occurrence of crop pests and diseases has largely increased in China since 1970[J]. Nature Food, 2021, 3: 57-65.
3	GOETZ A F H, VANE G, SOLOMON J E, et al. Imaging spectrometry for earth remote sensing[J]. Science, 1985, 228(4704): 1147-1153.
4	WEISS M, JACOB F, DUVEILLER G. Remote sensing for agricultural applications: A meta-review[J]. Remote Sensing of Environment, 2020, 236: ID 111402.
5	RADOGLOU-GRAMMATIKIS P, SARIGIANNIDIS P, LAGKAS T, et al. A compilation of UAV applications for precision agriculture[J]. Computer Networks, 2020, 172: ID 107148.
6	SISHODIA R P, RAY R L, SINGH S K. Applications of remote sensing in precision agriculture: A review[J]. Remote Sensing, 2020, 12(19): ID 3136.
7	PINTER JR P J, HATFIELD J L, SCHEPERS J S, et al. Remote sensing for crop management[J]. Photogrammetric Engineering & Remote Sensing, 2003, 69(6): 647-664.
8	WHITE J W, ANDRADE-SANCHEZ P, GORE M A, et al. Field-based phenomics for plant genetics research[J]. Field Crops Research, 2012, 133: 101-112.
9	WATTS A C, AMBROSIA V G, HINKLEY E A. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use[J]. Remote Sensing, 2012, 4(6): 1671-1692.
10	BAGHERI N. Application of aerial remote sensing technology for detection of fire blight infected pear trees[J]. Computers and Electronics in Agriculture, 2020, 168: ID 105147.
11	LAN Y, HUANG Z, DENG X, et al. Comparison of machine learning methods for citrus greening detection on UAV multispectral images[J]. Computers and Electronics in Agriculture, 2020, 171: ID 105234.
12	CHIVASA W, MUTANGA O, BIRADAR C. UAV-based multispectral phenotyping for disease resistance to accelerate crop improvement under changing climate conditions[J]. Remote Sensing, 2020, 12(15): ID 2445.
13	CHIVASA W, MUTANGA O, BURGUENO J. UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation[J]. Computers and Electronics in Agriculture, 2021, 184: ID 106128.
14	SUGIURA R, NOGUCHI N, ISHII K. Remote-sensing technology for vegetation monitoring using an unmanned helicopter[J]. Biosystems Engineering, 2005, 90(4): 369-379.
15	BERNI J A J, ZARCO-TEJADA P J, SUAREZ L, et al. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 722-738.
16	CORCOLES J I, ORTEGA J F, HERNANDEZ D, et al. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle[J]. Biosystems Engineering, 2013, 115(1): 31-42.
17	WAHAB I, HALL O, JIRSTROM M. Remote sensing of yields: Application of UAV imagery-derived NDVI for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa[J]. Drones, 2018, 2(3): ID 28.
18	YAO H, QIN R, CHEN X. Unmanned aerial vehicle for remote sensing applications—A review[J]. Remote Sensing, 2019, 11(12): ID 1443.
19	SAARI H, PELLIKKA I, PESONEN L, et al. Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications[C]// Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII. International Society for Optics and Photonics, Prague, Czech Republic: SPIE, 2011, 8174: ID 81740H.
20	SADEQ H A. Accuracy assessment using different UAV image overlaps[J]. Journal of Unmanned Vehicle Systems, 2019, 7(3): 175-193.
21	YANG G, LIU J, ZHAO C, et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives[J]. Frontiers in Plant Science, 2017, 8: ID 1111.
22	XIE C, YANG C. A review on plant high-throughput phenotyping traits using UAV-based sensors[J]. Computers and Electronics in Agriculture, 2020, 178: ID 105731.
23	XUE J, SU B. Significant remote sensing vegetation indices: A review of developments and applications[J]. Journal of Sensors, 2017, 2017: ID 1353691.
24	GITELSON A, ARKEBAUER T, VI?A A, et al. Evaluating plant photosynthetic traits via absorption coefficient in the photosynthetically active radiation region[J]. Remote Sensing of Environment, 2021, 258: ID 112401.
25	HAUSER L T, TIMMERMANS J, WINDT NVAN DER, et al. Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation[J]. Remote Sensing of Environment, 2021, 265: ID 112684.
26	HORNERO A, HERNáNDEZ-CLEMENTE R, NORTH P R J, et al. Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling[J]. Remote Sensing of Environment, 2020, 236: ID 111480.
27	PIGNATTI S, CASA R, LANEVE G, et al. Sino–EU earth observation data to support the monitoring and management of agricultural resources[J]. Remote Sensing, 2021, 13(15): ID 2889.
28	LEE J H, SHIN J, REALFF M J. Machine learning: Overview of the recent progresses and implications for the process systems engineering field[J]. Computers & Chemical Engineering, 2018, 114: 111-121.
29	LIU J, XIANG J, JIN Y, et al. Boost precision agriculture with unmanned aerial vehicle remote sensing and edge intelligence: A survey[J]. Remote Sensing, 2021, 13(21): ID 4387.
30	VOULODIMOS A, DOULAMIS N, DOULAMIS A, et al. Deep learning for computer vision: A brief review[J]. Computational Intelligence and Neuroscience, 2018, 2018: ID 7068349.
31	NAZIR M N M M, TERHEM R, NORHISHAM A R, et al. Early monitoring of health status of plantation-grown eucalyptus pellita at large spatial scale via visible spectrum imaging of canopy foliage using unmanned aerial vehicles[J]. Forests, 2021, 12(10): ID 1393.
32	DUTTA K, TALUKDAR D, BORA S S. Segmentation of unhealthy leaves in cruciferous crops for early disease detection using vegetative indices and Otsu thresholding of aerial images[J]. Measurement, 2022, 189: ID 110478.
33	MAES W H, STEPPE K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture[J]. Trends in Plant Science, 2019, 24(2): 152-164.
34	CALOU V B C, TEIXEIRA A D S, MOREIRA L C J, et al. The use of UAVs in monitoring yellow sigatoka in banana[J]. Biosystems Engineering, 2020, 193: 115-125.
35	DANG L M, HASSAN S I, SUHYEON I, et al. UAV based wilt detection system via convolutional neural networks[J]. Sustainable Computing: Informatics and Systems, 2020, 28: ID 100250.
36	HUANG H, DENG J, LAN Y, et al. Detection of helminthosporiumleaf blotch disease based on UAV imagery[J]. Applied Sciences, 2019, 9(3): ID 558.
37	DEL-CAMPO-SANCHEZ A, BALLESTEROS R, HERNANDEZ-LOPEZ D, et al. Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques[J]. PLoS One, 2019, 14(4): ID e0215521.
38	TETILA E C, MACHADO B B, ASTOLFI G, et al. Detection and classification of soybean pests using deep learning with UAV images[J]. Computers and Electronics in Agriculture, 2020, 179: ID 105836.
39	PADUA L, MARQUES P, MARTINS L, et al. Monitoring of chestnut trees using machine learning techniques applied to UAV-based multispectral data[J]. Remote Sensing, 2020, 12(18): ID 3032.
40	SU J, LIU C, HU X, et al. Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery[J]. Computers and electronics in agriculture, 2019, 167: ID 105035.
41	ATSHAN L A, BROWN P, XU C, et al. Early detection of disease infection in chilli crops using sensors[C]// International Horticultural Congress, Istanbul, Turkey: ISHS, 2018: 263-270.
42	YE H, HUANG W, HUANG S, et al. Recognition of banana fusarium wilt based on UAV remote sensing[J]. Remote Sensing, 2020, 12(6): ID 938.
43	KALISCHUK M, PARET M L, FREEMAN J H, et al. An improved crop scouting technique incorporating unmanned aerial vehicle–assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon[J]. Plant Disease, 2019, 103(7): 1642-1650.
44	MARSTON Z P D, CIRA T M, HODGSON E W, et al. Detection of stress induced by soybean aphid (Hemiptera: Aphididae) using multispectral imagery from unmanned aerial vehicles[J]. Journal of Economic Entomology, 2020, 113(2): 779-786.
45	VIERA-TORRES M, SINDE-GONZALEZ I, GIL-DOCAMPO M, et al. Generating the baseline in the early detection of bud rot and red ring disease in oil palms by geospatial technologies[J]. Remote Sensing, 2020, 12(19): ID 3229.
46	赵晋陵, 金玉, 叶回春, 等. 基于无人机多光谱影像的槟榔黄化病遥感监测[J]. 农业工程学报, 2020, 36(8): 54-61.
46	ZHAO J, JIN Y, YE H, et al. Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images[J]. Transactions of the CSAE, 2020, 36(8): 54-61.
47	LEI S, LUO J, TAO X, et al. Remote sensing detecting of yellow leaf disease of arecanut based on UAV multisource sensors[J]. Remote Sensing, 2021, 13(22): ID 4562.
48	DADRASJAVAN F, SAMADZADEGAN F, POURAZAR S H S, et al. UAV-based multispectral imagery for fast citrus greening detection[J]. Journal of Plant Diseases and Protection, 2019, 126(4): 307-318.
49	CHANG A, YEOM J, JUNG J, et al. Comparison of canopy shape and vegetation indices of citrus trees derived from UAV multispectral images for characterization of citrus greening disease[J]. Remote Sensing, 2020, 12(24): ID 4122.
50	CHEN T, YANG W, ZHANG H, et al. Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data[J]. Computers and Electronics in Agriculture, 2020, 177: ID 105708.
51	HEIM R H J, WRIGHT I J, SCARTH P, et al. Multispectral, aerial disease detection for myrtle rust (Austropuccinia psidii) on a lemon myrtle plantation[J]. Drones, 2019, 3(1): ID 25.
52	LEóN-RUEDA W A, LEóN C, CARO S G, et al. Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools[J]. Tropical Plant Pathology, 2021: 1-16.
53	SU J, YI D, SU B, et al. Aerial visual perception in smart farming: Field study of wheat yellow rust monitoring[J]. IEEE Transactions on Industrial Informatics, 2020, 17(3): 2242-2249.
54	DI NISIO A, ADAMO F, ACCIANI G, et al. Fast detection of olive trees affected by xylella fastidiosa from UAVs using multispectral imaging[J]. Sensors, 2020, 20(17): ID 4915.
55	马云强, 李宇宸, 刘梦盈, 等. 基于无人机多光谱影像的云南切梢小蠹危害监测反演研究[J]. 西南农业学报, 2021, 34(9): 1878-1884.
55	MA Y, LI Y, LIU M, et al. Harm monitoring and inversion study on Tomicus yunnanensis based on multi-spectral image of unmanned aerial vehicle[J]. Southwest China Journal of Agricultural Sciences, 2021,34(9): 1878-1184.
56	IZZUDDIN M A, HAMZAH A, NISFARIZA M N, et al. Analysis of multispectral imagery from unmanned aerial vehicle (UAV) using object-based image analysis for detection of Ganoderma disease in oil palm[J]. Journal of Oil Palm Research, 2020, 32(3): 497-508.
57	RODRíGUEZ J, LIZARAZO I, PRIETO F, et al. Assessment of potato late blight from UAV-based multispectral imagery[J]. Computers and Electronics in Agriculture, 2021, 184: ID 106061.
58	WANG T, THOMASSON J A, ISAKEIT T, et al. A plant-by-plant method to identify and treat cotton root rot based on UAV remote sensing[J]. Remote Sensing, 2020, 12(15): ID 2453.
59	ZHANG T, XU Z, SU J, et al. Ir-UNet: Irregular segmentation u-shape network for wheat yellow rust detection by UAV multispectral imagery[J]. Remote Sensing, 2021, 13(19): ID 3892.
60	马书英, 郭增长, 王双亭, 等. 板栗树红蜘蛛虫害无人机高光谱遥感监测研究[J]. 农业机械学报, 2021, 52(4): 171-180.
60	MA S, GUO Z, WANG S, et al. Hyperspectral remote sensing monitoring of Chinese chestnut red mite insect pests in UAV[J]. Transactions of the CSAM, 2021, 52(4): 171-180.
61	郭伟, 乔红波, 赵恒谦, 等. 基于比值导数法的棉花蚜害无人机成像光谱监测模型研究[J]. 光谱学与光谱分析, 2021, 41(5): 1543-1550.
61	GUO W, QIAO H, ZHAO H, et al. Cotton aphid damage monitoring using UAV hyperspectral data based on derivative of ratio spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(5): 1543-1550.
62	郑蓓君, 陈芸芝, 李凯, 等. 高光谱数据的刚竹毒蛾虫害程度检测[J]. 光谱学与光谱分析, 2021, 41(10): 3200-3207.
62	ZHENG P, CHEN Y, LI K, et al. Detection of pest degree of phyllostachys Chinese with hyperspectral data[J]. Spectroscopy and Spectral Analysis, 2021, 41(10): 3200-3207.
63	郭伟, 朱耀辉, 王慧芳, 等. 基于无人机高光谱影像的冬小麦全蚀病监测模型研究[J]. 农业机械学报, 2019, 50(9): 162-169.
63	GUO W, ZHU Y, WANG H, et al. Monitoring model of winter wheat take-all based on UAV hyperspectral imaging[J]. Transactions of the CSAM, 2019, 50(9): 162-169.
64	梁辉, 何敬, 雷俊杰. 无人机高光谱的玉米冠层大斑病监测[J]. 光谱学与光谱分析, 2020, 40(6): 1965-1972.
64	LIANG H, HE J, LEI J. Monitoring of corn canopy blight disease based on UAV hyperspectral method[J]. Spectroscopy and Spectral Analysis, 2020, 40(6): 1965-1972.
65	桑佳茂, 陈丰农. 基于光谱特征点秩和检验的稻曲病发病程度检测[J]. 光谱学与光谱分析, 2021, 41(10): 3214-3219.
65	SANG J, CHEN F. Detection of rice false smut grade degree based on the rank sum test of spectral feature points[J]. Spectroscopy and Spectral Analysis, 2021, 41(10): 3214-3219.
66	SONG P, ZHENG X, LI Y, et al. Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data[J]. Science of the Total Environment, 2020, 719: ID 137519.
67	LIU L, DONG Y, HUANG W, et al. Monitoring wheat Fusarium head blight using unmanned aerial vehicle hyperspectral imagery[J]. Remote Sensing, 2020, 12(22): ID 3811.
68	MA H, HUANG W, DONG Y, et al. Using UAV-based hyperspectral imagery to detect winter wheat fusarium head blight[J]. Remote Sensing, 2021, 13(15): ID 3024.
69	GUO A, HUANG W, DONG Y, et al. Wheat yellow rust detection using UAV-based hyperspectral technology[J]. Remote Sensing, 2021, 13(1): ID 123.
70	孔繁昌, 刘焕军, 于滋洋, 等. 高寒地区粳稻穗颈瘟的无人机高光谱遥感识别[J]. 农业工程学报, 2020, 36(22): 68-75.
70	KONG F, LIU H, YU Z, et al. Identification of japonica rice panicle blast in alpine region by UAV hyperspectral remote sensing[J]. Transactions of the CSAE, 2020, 36(22): 68-75.
71	DENG X, ZHU Z, YANG J, et al. Detection of citrus Huanglongbing based on multi-input neural network model of UAV hyperspectral remote sensing[J]. Remote Sensing, 2020, 12(17): ID 2678.
72	ABDULRIDHA J, BATUMAN O, AMPATZIDIS Y. UAV-based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning[J]. Remote Sensing, 2019, 11(11): ID 1373.
73	ABDULRIDHA J, AMPATZIDIS Y, ROBERTS P, et al. Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence[J]. Biosystems Engineering, 2020, 197: 135-148.
74	BOHNENKAMP D, BEHMANN J, MAHLEIN A K. In-field detection of yellow rust in wheat on the ground canopy and UAV scale[J]. Remote Sensing, 2019, 11(21): ID 2495.
75	ABDULRIDHA J, AMPATZIDIS Y, KAKARLA S C, et al. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques[J]. Precision Agriculture, 2020, 21(5): 955-978.
76	ABDULRIDHA J, AMPATZIDIS Y, QURESHI J, et al. Laboratory and UAV-based identification and classification of tomato yellow leaf curl, bacterial spot, and target spot diseases in tomato utilizing hyperspectral imaging and machine learning[J]. Remote Sensing, 2020, 12(17): ID 2732.
77	邓小玲, 曾国亮, 朱梓豪, 等. 基于无人机高光谱遥感的柑橘患病植株分类与特征波段提取[J]. 华南农业大学学报, 2020, 41(6): 100-108.
77	DENG X, ZENG G, ZHU Z, et al. Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing[J]. Journal of South China Agricultural University, 2020, 41(6): 100-108.
78	MACDONALD S L, STAID M, STAID M, et al. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards[J]. Computers and Electronics in Agriculture, 2016, 130: 109-117.
79	GARCIA-RUIZ F, SANKARAN S, MAJA J M, et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture, 2013, 91: 106-115.
80	MCCANN C, REPASKY K S, LAWRENCE R, et al. Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 131: 121-133.
81	ZHANG X, HAN L, DONG Y, et al. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images[J]. Remote Sensing, 2019, 11(13): ID 1554.
82	AN G, XING M, HE B, et al. Extraction of areas of rice false smut infection using UAV hyperspectral data[J]. Remote Sensing, 2021, 13(16): ID 3185.
83	XIAO Y, DONG Y, HUANG W, et al. Wheat fusarium head blight detection using UAV-based spectral and texture features in optimal window size[J]. Remote Sensing, 2021, 13(13): ID 2437.
84	FRANCESCONI S, HARFOUCHE A, MAESANO M, et al. UAV-based thermal, RGB imaging and gene expression analysis allowed detection of fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat[J]. Frontiers in Plant Science, 2021, 12: ID 551.
85	陈欣欣, 刘子毅, 吕美巧, 等. 基于热红外成像技术的油菜菌核病早期检测研究[J]. 光谱学与光谱分析, 2019, 39(3): 730-737.
85	CHEN X, LIU Z, LYU M, et al. Diagnosis and monitoring of sclerotinia stem rot of oilseed rape using thermal infrared imaging[J]. Spectroscopy and Spectral Analysis, 2019, 39(3): 730-737.
86	LIN Q, HUANG H, WANG J, et al. Detection of pine shoot beetle (PSB) stress on pine forests at individual tree level using UAV-based hyperspectral imagery and lidar[J]. Remote Sensing, 2019, 11(21): ID 2540.
87	BRIECHLE S, KRZYSTEK P, VOSSELMAN G. Classification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 2: 203-210.
88	SAVIAN F, MARTINI M, ERMACORA P, et al. Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing[J]. Remote Sensing, 2020, 12(14): ID 2194.
89	YU R, LUO Y, ZHOU Q, et al. A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 101: ID 102363.
90	SMIGAJ M, GAULTON R, SUAREZ J C, et al. Canopy temperature from an unmanned aerial vehicle as an indicator of tree stress associated with red band needle blight severity[J]. Forest Ecology and Management, 2019, 433: 699-708.
91	KERKECH M, HAFIANE A, CANALS R. VddNet: Vine disease detection network based on multispectral images and depth map[J]. Remote Sensing, 2020, 12(20): ID 3305.
92	KERKECH M, HAFIANE A, CANALS R. Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach[J]. Computers and Electronics in Agriculture, 2020, 174: ID 105446.
93	MORIYA é A S, IMAI N N, TOMMASELLI A M G, et al. Detection and mapping of trees infected with citrus gummosis using UAV hyperspectral data[J]. Computers and Electronics in Agriculture, 2021, 188: ID 106298.
94	赵晓阳, 张建, 张东彦, 等. 低空遥感平台下可见光与多光谱传感器在水稻纹枯病病害评估中的效果对比研究[J]. 光谱学与光谱分析, 2019, 39(4): 1192-1198.
94	ZHAO X, ZHANG J, ZHANG D, et al. Comparison between the effects of visible light and multispectral sensor based on low-altitude remote sensing platform in the evaluation of rice sheath blight[J]. Spectroscopy and Spectral Analysis, 2019, 39(4): 1192-1198.
95	DANG L, WANG H, LI Y, et al. Fusarium wilt of radish detection using RGB and near infrared images from unmanned aerial vehicles[J]. Remote Sensing, 2020, 12(17): ID 2863.
96	AWAIS M, LI W, CHEEMA M J M, et al. Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions[J]. Environmental Technology & Innovation, 2021, 22: ID 101465.
97	FENG L, CHEN S, ZHANG C, et al. A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping[J]. Computers and Electronics in Agriculture, 2021, 182: ID 106033.
98	RUBIO-HERVAS J, GUPTA A, ONG Y S. Data-driven risk assessment and multicriteria optimization of UAV operations[J]. Aerospace Science and Technology, 2018, 77: 510-523.
99	HUSSEIN M, NOUACER R, CORRADI F, et al. Key technologies for safe and autonomous drones[J]. Microprocessors and Microsystems, 2021, 87: ID 104348.
100	MUKHERJEE A, MISRA S, RAGHUWANSHI N S. A survey of unmanned aerial sensing solutions in precision agriculture[J]. Journal of Network and Computer Applications, 2019, 148: ID 102461.
101	黄文江, 师越, 董莹莹, 等. 作物病虫害遥感监测研究进展与展望[J]. 智慧农业, 2019, 1(4): 1-11.
101	HUANG W, SHI Y, DONG Y, et al. Progress and prospects of crop diseases and pests monitoring by remote sensing[J]. Smart Agriculture, 2019, 1(4): 1-11.
102	MIGNONI M E, HONORATO A, KUNST R, et al. Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification[J]. Data in Brief, 2021: ID 107756.
103	OUHAMI M, HAFIANE A, ES-SAADY Y, et al. Computer vision, IoT and data fusion for crop disease detection using machine learning: A survey and ongoing research[J]. Remote Sensing, 2021, 13(13): ID 2486.
104	LI F, LIU Z, SHEN W, et al. A remote sensing and airborne edge-computing based detection system for pine wilt disease[J]. IEEE Access, 2021, 9: 66346-66360.
105	兰玉彬, 邓小玲, 曾国亮. 无人机农业遥感在农作物病虫草害诊断应用研究进展[J]. 智慧农业, 2019, 1(2): 1-19.
105	LAN Y, DENG X, ZENG G. Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing[J]. Smart Agriculture, 2019, 1(2): 1-19.
106	ELNABTY I A, FAHMY Y, KAFAFY M. A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks[J]. Physical Communication, 2021: ID 101564.
107	MISHRA D, NATALIZIO E. A survey on cellular-connected UAVs: Design challenges, enabling 5G/B5G innovations, and experimental advancements[J]. Computer Networks, 2020, 182: ID 107451.

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无人机遥感监测作物病虫害胁迫方法与最新研究进展

Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle

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