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[98] Du H, Wang W, Xu CW, Xiao R, Sun CY. Real-time onboard 3D state estimation of an unmanned aerial vehicle in multi-environments
using multi-sensor data fusion. Sensors, 2020, 20(3): 919. [doi: 10.3390/s20030919]
[99] Lee JC, Chen CC, Shen CT, Lai YC. Landmark-based scale estimation and correction of visual inertial odometry for VTOL UAVs in a
GPS-denied environment. Sensors, 2022, 22(24): 9654. [doi: 10.3390/s22249654]
[100] Dong X, Gao YZ, Guo JL, Zuo SY, Xiang JW, Li DC, Tu Z. An integrated UWB-IMU-vision framework for autonomous approaching
and landing of UAVs. Aerospace, 2022, 9(12): 797. [doi: 10.3390/aerospace9120797]
[101] Zhang KY, Chen PP, Ma TB, Gao SW. On-demand precise tracking for energy-constrained UAVs in underground coal mines. IEEE
Trans. on Instrumentation and Measurement, 2022, 71: 5500814. [doi: 10.1109/TIM.2022.3146925]
[102] Xu XG, Fan LL, Li ZH, Meng Y, Feng HK, Yang H, Xu B. Estimating leaf nitrogen content in corn based on information fusion of
multiple-sensor imagery from UAV. Remote Sensing, 2021, 13(3): 340. [doi: 10.3390/rs13030340]
[103] Li JT, Schachtman DP, Creech CF, Wang L, Ge YF, Shi YY. Evaluation of UAV-derived multimodal remote sensing data for biomass
prediction and drought tolerance assessment in bioenergy sorghum. The Crop Journal, 2022, 10(5): 1363–1375. [doi: 10.1016/j.cj.2022.
04.005]
[104] Cheng MH, Jiao XY, Liu YD, Shao MC, Yu X, Bai Y, Wang ZX, Wang SY, Tuohuti N, Liu SB, Shi L, Yin DM, Huang X, Nie CW, Jin
XL. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning.
Agricultural Water Management, 2022, 264: 107530. [doi: 10.1016/j.agwat.2022.107530]
[105] Zhang SH, Duan JZ, He L, Jing YH, Schulthess UC, Lashkari A, Guo TC, Wang YH, Feng W. Wheat yield estimation from UAV
platform based on multi-modal remote sensing data fusion. Acta Agronomica Sinica, 2022, 48(7): 1746–1760 (in Chinese with English
abstract). [doi: 10.3724/SP.J.1006.2022.11053]
[106] Zhang Y, Yang YZ, Zhang QW, Duan RQ, Liu JQ, Qin YC, Wang XZ. Toward multi-stage phenotyping of soybean with multimodal
UAV sensor data: A comparison of machine learning approaches for leaf area index estimation. Remote Sensing, 2022, 15(1): 7. [doi: 10.
3390/rs15010007]
[107] Zhang LC, Sun BS, Zhao DN, Shan CF, Wang GB, Song CC, Chen PC, Lan YB. Prediction of cotton FPAR and construction of
defoliation spraying prescription map based on multi-source UAV images. Computers and Electronics in Agriculture, 2024, 220:
108897. [doi: 10.1016/j.compag.2024.108897]
[108] Wu S, Deng L, Guo LJ, Wu YJ. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle
imagery. Plant Methods, 2022, 18(1): 68. [doi: 10.1186/s13007-022-00899-7]
[109] Liu SB, Jin XL, Nie CW, Wang SY, Yu X, Cheng MH, Shao MC, Wang ZX, Tuohuti N, Bai Y, Liu YD. Estimating leaf area index
using unmanned aerial vehicle data: Shallow vs. deep machine learning algorithms. Plant Physiology, 2021, 187(3): 1551–1576. [doi: 10.
1093/plphys/kiab322]
[110] Xia FL, Quan LZ, Lou ZX, Sun D, Li HL, Lv XL. Identification and comprehensive evaluation of resistant weeds using unmanned
aerial vehicle-based multispectral imagery. Frontiers in Plant Science, 2022, 13: 938604. [doi: 10.3389/fpls.2022.938604]
[111] Ma JC, Liu BH, Ji L, Zhu ZC, Wu YF, Jiao WH. Field-scale yield prediction of winter wheat under different irrigation regimes based on
dynamic fusion of multimodal UAV imagery. Int’l Journal of Applied Earth Observation and Geoinformation, 2023, 118: 103292. [doi:
10.1016/j.jag.2023.103292]
[112] Xu R, Li CY, Bernardes S. Development and testing of a UAV-based multi-sensor system for plant phenotyping and precision
agriculture. Remote Sensing, 2021, 13(17): 3517. [doi: 10.3390/rs13173517]
[113] López A, Jurado JM, Ogayar CJ, Feito FR. A framework for registering UAV-based imagery for crop-tracking in precision agriculture.
Int’l Journal of Applied Earth Observation and Geoinformation, 2021, 97: 102274. [doi: 10.1016/j.jag.2020.102274]
[114] Ni M, Wang HJ, Liu XD, Liao YL, Fu L, Wu QQ, Mu J, Chen XY, Li J. Design of variable spray system for plant protection UAV
based on CFD simulation and regression analysis. Sensors, 2021, 21(2): 638. [doi: 10.3390/s21020638]
[115] Almeida DRAD, Broadbent EN, Ferreira MP, et al. Monitoring restored tropical forest diversity and structure through UAV-borne
hyperspectral and LiDAR fusion. Remote Sensing of Environment, 2021, 264: 112582. [doi: 10.1016/j.rse.2021.112582]
[116] Campbell MJ, Dennison PE, Tune JW, Kannenberg SA, Kerr KL, Codding BF, Anderegg WRL. A multi-sensor, multi-scale approach to
mapping tree mortality in woodland ecosystems. Remote Sensing of Environment, 2020, 245: 111853. [doi: 10.1016/j.rse.2020.111853]
[117] Tauro F, Maltese A, Giannini R, Harfouche A. Latent heat flux variability and response to drought stress of black poplar: A multi-
platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck. Remote Sensing of Environment,
2022, 268: 112771. [doi: 10.1016/j.rse.2021.112771]
[118] Moradi F, Javan FD, Samadzadegan F. Potential evaluation of visible-thermal UAV image fusion for individual tree detection based on
convolutional neural network. Int’l Journal of Applied Earth Observation and Geoinformation, 2022, 113: 103011. [doi: 10.1016/j.jag.
2022.103011]
[119] Zhong H, Zhang ZY, Liu HR, Wu JZ, Lin WS. Individual tree species identification for complex coniferous and broad-leaved mixed