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, REAL |
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