近地遥感技术在大田作物株高测量中的研究现状与展望

doi:10.12133/j.smartag.2021.3.1.202102-SA033

摘要/Abstract

摘要：

株高是动态衡量作物健康和整体生长状况的关键指标，广泛用于估测作物的生物学产量和最终籽粒产量。传统的人工测量方式存在规模小、效率低以及耗时长等问题。近十年来，近地遥感技术在农业领域发展迅速，使得高精度、高频次、高效率的作物株高采集成为可能。本文首先回顾了国内外基于遥感手段获取株高研究的论文发表情况；其次对获取株高的不同平台以及传感器的基本原理、优势及其局限性进行了介绍和评述，重点论述了激光雷达和可见光相机两种传感器的测高流程与涉及的关键技术；在此基础上归纳了株高在作物生物量估算、倒伏监测、产量预测和辅助育种等方面的应用研究进展；最后对近地遥感技术在株高获取上存在的问题进行讨论分析，并从测高平台和传感器、裸土探测和插值算法、株高应用研究及农学与遥感测高差异四个方向进行了展望，可为今后近地遥感测高的研究与方法应用提供参考。

关键词: 株高, 近地遥感, 作物, 无人机, 可见光相机, 激光雷达

Abstract:

Plant height is a key indicator to dynamically measure crop health and overall growth status, which is widely used to estimate the biological yield and final grain yield of crops. The traditional manual measurement method is subjective, inefficient, and time-consuming. And the plant height obtained by sampling cannot evaluate the height of the whole field. In the last decade, remote sensing technology has developed rapidly in agriculture, which makes it possible to collect crop height information with high accuracy, high frequency, and high efficiency. This paper firstly reviewed the literature on obtaining plant height by using remote sensing technology for understanding the research progress of height estimation in the field. Unmanned aerial vehicle (UAV) platform with visible-light camera and light detection and ranging (LiDAR) were the most frequently used methods. And main research crops included wheat, corn, rice, and other staple food crops. Moreover, crop height measurement was mainly based on near-field remote sensing platforms such as ground, UAV, and airborne. Secondly, the basic principles, advantages, and limitations of different platforms and sensors for obtaining plant height were analyzed. The altimetry process and the key techniques of LiDAR and visible-light camera were discussed emphatically, which included extraction of crop canopy and soil elevation information, and feature matching of the imaging method. Then, the applications using plant height data, including the inversion of biomass, lodging identification, yield prediction, and breeding of crops were summarized. However, the commonly used empirical model has some problems such large measured data, unclear physical significance, and poor universality. Finally, the problems and challenges of near-field remote sensing technology in plant height acquisition were proposed. Selecting appropriate data to meet the needs of cost and accuracy, improving the measurement accuracy, and matching the plant height estimation of remote sensing with the agricultural application need to be considered. In addition, we prospected the future development was prospected from four aspects of 1) platform and sensor, 2) bare soil detection and interpolation algorithm, 3) plant height application research, and 4) the measurement difference of plant height between agronomy and remote sensing, which can provide references for future research and method application of near-field remote sensing height measurement.

Key words: plant height, near-field remote sensing, crop, unmanned aerial vehicle, visible-light camera, LiDAR

中图分类号:

S127

张建, 谢田晋, 杨万能, 周广生. 近地遥感技术在大田作物株高测量中的研究现状与展望[J]. 智慧农业(中英文), 2021, 3(1): 1-15.

ZHANG Jian, XIE Tianjin, YANG Wanneng, ZHOU Guangsheng. Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology[J]. Smart Agriculture, 2021, 3(1): 1-15.

参考文献

1	PENG J, RICHARDS D E, HARTLEY N M, et al. 'Green revolution' genes encode mutant gibberellin response modulators[J]. Nature, 1999, 400: 256-261.
2	SASAKI A, ASHIKARI M, UEGUCHI-TANAKA M, et al. Green revolution: A mutant gibberellin-synthesis gene in rice[J]. Nature, 2002, 416: 701-702.
3	KUMAR K, NEELAM K, BHATIA D, et al. High resolution genetic mapping and identification of a candidate gene(s) for the purple sheath color and plant height in an interspecific F-2 population derived from Oryza nivara Sharma & Shastry × Oryza sativa L. cross[J]. Genetic Resources and Crop Evolution, 2020, 67(1): 97-105.
4	LIU K, DONG X, QIU B, et al. Analysis of cotton height spatial variability based on UAV-LiDAR[J]. International Journal of Precision Agricultural Aviation, 2020, 3(3): 72-76.
5	刘治开, 牛亚晓, 王毅, 等. 基于无人机可见光遥感的冬小麦株高估算[J]. 麦类作物学报, 2019, 39(7): 859-866.
5	LIU Z, NIU Y, WANG Y, et al. Estimation of plant height of winter wheat based on UAV visible image[J]. Journal of Triticeae Crops, 2019, 39(7): 859-866.
6	黄瑞冬, 李广权. 玉米株高整齐度及其测定方法的比较[J]. 玉米科学, 1995, 3(2): 61-63.
6	HUANG R, LI G. Plant height consistencies in maize population and a comparison of their measuring techniques[J]. Maize Science, 1995, 3(2): 61-63.
7	赵广才. 关于调查小麦株高标准的讨论[J]. 北京农业科学, 1996, 14(1): 18.
7	ZHAO G. Discussion on investigating high standard of wheat plant[J]. Beijing Agricultural Sciences, 1996, 14(1): 18.
8	刘建刚, 赵春江, 杨贵军, 等. 无人机遥感解析田间作物表型信息研究进展[J]. 农业工程学报, 2016, 32(24): 98-106.
8	LIU J, ZHAO C, YANG G, et al. Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J]. Transactions of the CSAE, 2016, 32(24): 98-106.
9	HMIDA S BEN, KALLEL A, PGASTELLU-ETCHEGORRY J, et al. Crop biophysical properties estimation based on LiDAR full-waveform inversion using the DART RTM[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(11): 4853-4868.
10	陈松尧, 程新文. 机载LiDAR系统原理及应用综述[J]. 测绘工程, 2007, 16(1): 27-31.
10	CHEN S, CHENG X. The principle and application of airborne LiDAR[J]. Engineering of Surveying and Mapping, 2007, 16(1): 27-31.
11	LI W, NIU Z, WANG C, et al. Combined use of airborne LiDAR and satellite GF-1 data to estimate leaf area index, height, and aboveground biomass of maize during peak growing season[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(9): 4489-4501.
12	EITEL J U, H?FLE B, VIERLING L A, et al. Beyond 3-D: The new spectrum of LiDAR applications for earth and ecological sciences[J]. Remote Sensing of Environment, 2016, 186: 372-392.
13	FRIEDLI M, KIRCHGESSNER N, GRIEDER C, et al. Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions[J]. Plant Methods, 2016, 12: ID 9.
14	CROMMELINCK S, HOEFLE B. Simulating an autonomously operating low-cost static terrestrial LiDAR for multitemporal maize crop height measurements[J]. Remote Sensing, 2016, 8(3): ID 205.
15	EITEL J U H, MAGNEY T S, VIERLING L A, et al. An automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal LiDAR[J]. Remote Sensing of Environment, 2016, 187: 414-422.
16	QIU Q, SUN N, BAI H, et al. Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a "Phenomobile"[J]. Frontiers in Plant Science, 2019, 10: ID 554.
17	ANDUJAR D, ESCOLA A, ROSELL-POLO J R, et al. Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops[J]. Computers and Electronics in Agriculture, 2013, 92: 11-15.
18	MALAMBO L, POPESCU S C, MURRAY S C, et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 64: 31-42.
19	苏伟, 蒋坤萍, 郭浩, 等. 地基激光雷达提取大田玉米植株表型信息[J]. 农业工程学报, 2019, 35(10): 125-130.
19	SU W, JIANG K, GUO H, et al. Extraction of phenotypic information of maize plants in field by terrestrial laser scanning[J]. Transactions of the CSAE, 2019, 35(10): 125-130.
20	YUAN W, LI J, BHATTA M, et al. Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS[J]. Sensors, 2018, 18(11): ID 3731.
21	MADEC S, BARET F, DE SOLAN B, et al. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR Estimates[J]. Frontiers in Plant Science, 2017, 8: ID 2002.
22	PHAN A T T, TAKAHASHI K, RIKIMARU A, et al. Method for estimating rice plant height without ground surface detection using laser scanner measurement[J]. Journal of Applied Remote Sensing, 2016, 10(4): ID 046018.
23	JIMENEZ-BERNI J A, DEERY D M, PABLO R L, et al. Throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR[J]. Frontiers in Plant Science, 2018, 9: ID 237.
24	WANG X, SINGH D, MARLA S, et al. Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies[J]. Plant Methods, 2018, 14: ID 53.
25	BARKER J, ZHANG N, SHARON J, et al. Development of a field-based high-throughput mobile phenotyping platform[J]. Computers and Electronics in Agriculture, 2016, 122: 74-85.
26	HARKEL T J, BARTHOLOMEUS H, KOOISTRA L, et al. Biomass and crop height estimation of different crops using UAV-based LiDAR[J]. Remote Sensing, 2020, 12(1): ID 17.
27	THOMPSON A L, THORP K R, CONLEY M M, et al. Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton[J]. Remote Sensing, 2019, 11: ID 700.
28	SCHIRRMANN M, HAMDORF A, GIEBEL A, et al. Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery[J]. Remote Sensing, 2017, 9(7): ID 665.
29	YUAN H, BENNETT R S, WANG N, et al. Development of a peanut canopy measurement system using a ground-based LiDAR sensor[J]. Frontiers in Plant Science, 2019, 10: ID 203.
30	BARMEIER G, MISTELE B, SCHMIDHALTER U, et al. Referencing laser and ultrasonic height measurements of barley cultivars by using a herbometre as standard[J]. Crop & Pasture Science, 2016, 67(12): 1215-1222.
31	PITTMAN J J, ARNALL D B, INTERRANTE S M, et al. Estimation of biomass and canopy height in bermudagrass, alfalfa, and wheat using ultrasonic, laser, and spectral sensors[J]. Sensors, 2015, 15(2): 2920-2943.
32	冯佳睿, 马晓丹, 关海鸥, 等. 基于深度信息的大豆株高计算方法[J]. 光学学报, 2019, 39(5): 258-268.
32	FENG J, MA X, GUAN H, et al. Calculation method of soybean plant height based on depth information[J]. Acta Optica Sinica, 2019, 39(5): 258-268.
33	MARTINEZ-GUANTER J, RIBEIRO A, PETEINATOS G G, et al. Low-cost three-dimensional modeling of crop plants[J]. Sensors, 2019, 19: ID 2883.
34	VAZQUEZ-ARELLANO M, PARAFOROS D S, REISER D, et al. Determination of stem position and height of reconstructed maize plants using a time-of-flight camera[J]. Computers and Electronics in Agriculture, 2018, 154: 276-288.
35	HAEMMERLE M, HOEFLE B. Mobile low-cost 3D camera maize crop height measurements under field conditions[J]. Precision Agriculture, 2018, 19(4): 630-647.
36	MA X, ZHU K, GUAN H, et al. High-throughput phenotyping analysis of potted soybean plants using colorized depth images based on a proximal platform[J]. Remote Sensing, 2019, 11(9): ID 1085.
37	XIONG X, YU L, YANG W, et al. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage[J]. Plant Methods, 2017, 13: ID 7.
38	MANO M. Precise and continuous measurement of plant heights in an agricultural field using a time-lapse camera[J]. Journal of Agricultural Meteorology, 2017, 73(3): 100-108.
39	SRITARAPIPAT T, RAKWATIN P, KASETKASEM T, et al. Automatic rice crop height measurement using a field server and digital image processing[J]. Sensors, 2014, 14(1): 900-926.
40	CAI J, KUMAR P, CHOPIN J, et al. Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images[J]. PloS One, 2018, 13(5): ID e0196671.
41	BROCKS S, BARETH G. Evaluating dense 3D reconstruction software packages for oblique monitoring of crop canopy surface[C]// The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, Czech Republic: XXIII ISPRS Congress, 2016: 785-789.
42	ZHANG Y, TENG P, AONO M, et al. 3D monitoring for plant growth parameters in field with a single camera by multi-view approach[J]. Journal of Agricultural Meteorology, 2018, 74(4): 129-139.
43	BENDIG J, BOLTEN A, BARETH G, et al. UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability[J]. Photogramm Fernerkund Geoinf, 2013, 47(6): 551-562.
44	HOLMAN F H, RICHE A B, MICHALSKI A, et al. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing[J]. Remote Sensing, 2016, 8(12): ID 1031.
45	CHANG A, JUNG J, MAEDA M M, et al. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS)[J]. Computers and Electronics in Agriculture, 2017, 141: 232-237.
46	陶惠林, 徐良骥, 冯海宽, 等. 基于无人机数码影像的冬小麦株高和生物量估算[J]. 农业工程学报, 2019, 35(19): 107-116.
46	TAO H, XU L, FENG H, et al. Estimation of plant height and biomass of winter wheat based on UAV digital image[J]. Transactions of the CSAE, 2019, 35(19): 107-116.
47	LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
48	BELTON D, HELMHOLZ P, LONG J, et al. Crop height monitoring using a consumer-grade camera and UAV technology[J]. Journal of Photogrammetry Remote Sensing and Geoinformation Science, 2019, 87: 249-262.
49	HAN L, YANG G, DAI H, et al. Fuzzy clustering of maize plant-height patterns using time series of UAV remote-sensing images and variety traits[J]. Frontiers in Plant Science, 2019, 10: ID 926.
50	TIRADO S B, HIRSCH C N, SPRINGER N M, et al. UAV-based imaging platform for monitoring maize growth throughout development[J]. Plant Direct, 2020, 4(6): ID e00230.
51	牛庆林, 冯海宽, 杨贵军, 等. 基于无人机数码影像的玉米育种材料株高和LAI监测[J]. 农业工程学报, 2018, 34(5): 73-82.
51	NIU Q, FENG H, YANG G, et al. Monitoring plant height and leaf area index of maize breeding material based on UAV digital images[J]. Transactions of the CSAE, 2018, 34(5): 73-82.
52	LIU H, ZHANG J, PAN Y, et al. An efficient approach based on UAV orthographic imagery to map paddy with support of field-level canopy height from point cloud data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(6): 2034-2046.
53	HU P, CHAPMAN S C, WANG X, et al. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding[J]. European Journal of Agronomy, 2018, 95: 24-32.
54	HAN X, THOMASSON J A, BAGNALL G C, et al. Measurement and calibration of plant-height from fixed-wing UAV images[J]. Sensors, 2018, 18(12): ID 4092.
55	SONG Y, WANG J. Winter wheat canopy height extraction from UAV-based point cloud data with a moving cuboid filter[J]. Remote Sensing, 2019, 11(5): ID 1239.
56	BORRA-SERRANO I, DE SWAEF T, QUATAERT P, et al. Closing the phenotyping gap: High resolution UAV time series for soybean growth analysis provides objective data from field trials[J]. Remote Sensing, 2020, 12(10): ID 1644.
57	XU R, LI C, PATERSON A H, et al. Multispectral imaging and unmanned aerial systems for cotton plant phenotyping[J]. PloS One, 2019, 14(2): ID e0205083.
58	WU M, YANG C, SONG X, et al. Evaluation of orthomosics and digital surface models derived from aerial imagery for crop type mapping[J]. Remote Sensing, 2017, 9(3): ID 239.
59	GUO T, FANG Y, CHENG T, et al. Detection of wheat height using optimized multi-scan mode of LiDAR during the entire growth stages[J]. Computers and Electronics in Agriculture, 2019, 165: ID 104959.
60	HOFFMEISTER D, WALDHOFF G, KORRES W, et al. Crop height variability detection in a single field by multi-temporal terrestrial laser scanning[J]. Precision Agriculture, 2016, 17(3): 296-312.
61	郭新年, 周恒瑞, 张国良, 等. 基于激光视觉的农作物株高测量系统[J]. 农业机械学报, 2018, 49(2): 22-27.
61	GUO X, ZHOU H, ZHANG G, et al. Crop height measurement system based on laser vision[J]. Transactions of the CSAM, 2018, 49(2): 22-27.
62	程曼, 蔡振江, 袁洪波, 等. 基于地面激光雷达的田间花生冠层高度测量系统研制[J]. 农业工程学报, 2019, 35(1): 180-187.
62	CHENG M, CAI Z, YUAN H, et al. System design for peanut canopy height information acquisition based on LiDAR[J]. Transactions of the CSAE, 2019, 35(1): 180-187.
63	TAO H, FENG H, XU L, et al. Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images[J]. Sensors, 2020, 20(4): ID 1231.
64	HAN L, YANG G, YANG H, et al. Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach[J]. Frontiers in Plant Science, 2018, 9: ID 1638.
65	VARELA S, ASSEFA Y, PRASAD P V V, et al. Spatio-temporal evaluation of plant height in corn via unmanned aerial systems[J]. Journal of Applied Remote Sensing, 2017, 11(3): ID 036013.
66	MHLANGA B, CHAUHAN B S, THIERFELDER C, et al. Weed management in maize using crop competition: A review[J]. Crop Protection, 2016, 88: 28-36.
67	YOUNGERMAN C Z, DITOMMASO A, CURRAN W S, et al. Corn density effect on interseeded cover crops, weeds, and grain yield[J]. Agronomy Journal, 2018, 110(6): 2478-2487.
68	ENCISO J, AVILA C A, JUNG J, et al. Validation of agronomic UAV and field measurements for tomato varieties[J]. Computers and Electronics in Agriculture, 2019, 158: 278-283.
69	BENDIG J, BOLTEN A, BENNERTZ S, et al. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging[J]. Remote Sensing, 2014, 6(11): 10395-10412.
70	POUND M P, FRENCH A P, MURCHIE E H, et al. Automated recovery of three-dimensional models of plant shoots from multiple color images[J]. Plant Physiology, 2014, 166(4): 1688-1698.
71	HASHEMINASAB S M, ZHOU T, HABIB A, et al. GNSS/INS-Assisted structure from motion strategies for UAV-Based imagery over mechanized agricultural fields[J]. Remote Sensing, 2020, 12(3): ID 351.
72	DANDRIFOSSE S, BOUVRY A, LEEMANS V, et al. Imaging wheat canopy through stereo vision: Overcoming the challenges of the laboratory to field transition for morphological features extraction[J]. Frontiers in Plant Science, 2020, 11: ID 96.
73	邱小雷, 方圆, 郭泰, 等. 基于地基LiDAR高度指标的小麦生物量监测研究[J]. 农业机械学报, 2019, 50(10): 159-166.
73	QIU X, FANG Y, GUO T, et al. Monitoring of wheat biomass based on terrestrial-LiDAR height metric[J]. Transactions of the CSAM, 2019, 50(10): 159-166.
74	BUELVAS R M, ADAMCHUK V I, LEKSONO E, et al. Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors[J]. Computers and Electronics in Agriculture, 2019, 164: ID 104896.
75	YUE J, YANG G, LI C, et al. Estimation of winter wheat above-ground biomass using Unmanned Aerial Vehicle-based snapshot hyperspectral sensor and crop height improved models[J]. Remote Sensing, 2017, 9(7): ID 708.
76	CEN H, WAN L, ZHU J, et al. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras[J]. Plant Methods, 2019, 15: ID 32.
77	BALLESTEROS R, FERNANDO ORTEGA J, HERNANDEZ D, et al. Onion biomass monitoring using UAV-based RGB imaging[J]. Precision Agriculture, 2018, 19(5): 840-857.
78	ZHOU L, GU X, CHENG S, et al. Analysis of plant height changes of lodged maize using UAV-LiDAR data[J]. Agriculture, 2020, 10(5): ID 146.
79	CHU T, STAREK M J, BREWER M J, et al. Assessing lodging severity over an experimental maize (Zea mays L.) field using UAS images[J]. Remote Sensing, 2017, 9(9): ID 923.
80	WILKE N, SIEGMANN B, KLINGBEIL L, et al. Quantifying lodging percentage and lodging severity using a UAV-based canopy height model combined with an objective threshold approach[J]. Remote Sensing, 2019, 11(5): ID 515.
81	GEIPEL J, LINK J, CLAUPEIN W, et al. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[J]. Remote Sensing, 2014, 6(11): 10335-10355.
82	YU D, ZHA Y, SHI L, et al. Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations[J]. European Journal of Agronomy, 2020, 121: ID 126159.
83	FENG A, ZHOU J, VORIES E D, et al. Yield estimation in cotton using UAV-based multi-sensor imagery[J]. Biosystems Engineering, 2020, 193: 101-114.
84	LI B, XU X, ZHANG L, et al. Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2020, 162: 161-172.
85	HASSAN M A, YANG M, FU L, et al. Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat[J]. Plant Methods, 2019, 15: ID 37.
86	WANG X, ZHANG R, SONG W, et al. Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)[J]. Scientific Reports, 2019, 9: ID 3458.
87	ROTH L, STREIT B. Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: An applied photogrammetric approach[J]. Precision Agriculture, 2018, 19(1): 93-114.
88	BENDIG J, YU K, AASEN H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87.
89	LI J, SHI Y, VEERANAMPALAYAM-SIVAKUMAR AN, et al. Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system[J]. Frontiers in Plant Science, 2018, 9: ID 1406.
90	MICHEZ A, BAUWENS S, BROSTAUX Y, et al. How far can consumer-grade uav rgb imagery describe crop production? A 3D and multitemporal modeling approach applied to Zea mays[J]. Remote Sensing, 2018, 10(11): ID 1798.
91	HAN L, YANG G, DAI H, et al. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data[J]. Plant Methods, 2019, 15: ID 10.
92	ZHU W, SUN Z, PENG J, et al. Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales[J]. Remote Sensing, 2019, 11(22): ID 2678.
93	GREAVES H E, VIERLING L A, EITEL J U H, et al. Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR[J]. Remote Sensing of Environment, 2015, 164: 26-35.
94	GIL-DOCAMPO M L, ARZA-GARCIA M, ORTIZ-SANZ J, et al. Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry[J]. Geocarto International, 2020, 35(7): 687-699.
95	杨琦, 叶豪, 黄凯, 等. 利用无人机影像构建作物表面模型估测甘蔗LAI[J]. 农业工程学报, 2017, 33(8): 104-111.
95	YANG Q, YE H, HUANG K, et al. Estimation of leaf area index of sugarcane using crop surface model based on UAV image[J]. Transactions of the CSAE, 2017, 33(8): 104-111.
96	SINGH D, WANG X, KUMAR U, et al. High-throughput phenotyping enabled genetic dissection of crop lodging in wheat[J]. Frontiers in Plant Science, 2019, 10: ID 394.
97	SU W, ZHANG M, BIAN D, et al. Phenotyping of corn plants using Unmanned Aerial Vehicle (UAV) images[J]. Remote Sensing, 2019, 11(17): ID 2021.
98	HAN L, YANG G, FENG H, et al. Quantitative identification of maize lodging-causing feature factors using unmanned aerial vehicle images and a nomogram computation[J]. Remote Sensing, 2018, 10(10): ID 1528.
99	ACORSI M G, MARTELLO M, ANGNES G, et al. Identification of maize lodging: A case study using a remotely piloted aircraft system[J]. Engenharia Agricola, 2019, 39: 66-73.
100	LI J, VEERANAMPALAYAM-SIVAKUMAR AN, BHATTA M, et al. Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery[J]. Plant Methods, 2019, 15: ID 123.
101	ZHOU G, YIN X. Relationship of cotton nitrogen and yield with normalized difference vegetation index and plant height[J]. Nutrient Cycling in Agroecosystems, 2014, 100(2): 147-160.
102	XUE H, TIAN X, ZHANG K, et al. Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population[J]. PloS One, 2019, 14(11): ID e0224897.
103	HERTER C P, EBMEYER E, KOLLERS S, et al. Rht24 reduces height in the winter wheat population 'Solitar x Bussard' without adverse effects on Fusarium head blight infection[J]. Theoretical and Applied Genetics, 2018, 131(6): 1263-1272.
104	MA X, FENG F, WEI H, et al. Genome-wide association study for plant height and grain yield in rice under contrasting moisture regimes[J]. Frontiers in Plant Science, 2016, 7: ID 1801.
105	WATANABE K, GUO W, ARAI K, et al. High-throughput phenotyping of sorghum plant height using an Unmanned Aerial Vehicle and its application to genomic prediction modeling[J]. Frontiers in Plant Science, 2017, 8: ID 421.
106	KAKERU W, WEI G, KEIGO A, et al. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling[J]. Frontiers in Plant Science, 2017, 8: ID 421.
107	刘忠, 万炜, 黄晋宇, 等. 基于无人机遥感的农作物长势关键参数反演研究进展[J]. 农业工程学报, 2018, 34(24): 60-71.
107	LIU Z, WAN W, HUANG J, et al. Progress on key parameters inversion of crop growth based on unmanned aerial vehicle remote sensing[J]. Transactions of the CSAE, 2018, 34(24): 60-71.
108	XIE T, LI J, YANG C, et al. Crop height estimation based on UAV images: methods, errors, and strategies[J]. Computers and Electronics in Agriculture, 2021, 185: ID 106155.
109	WALTER J D C, EDWARDS J, MCDONALD G, et al. Estimating biomass and canopy height with LiDAR for field crop breeding[J]. Frontiers in Plant Science, 2019, 10: ID 1145.
110	WANG H, WANG R, LIU B, et al. QTL analysis of salt tolerance in Sorghum bicolor during whole—plant growth stages[J]. Plant Breeding, 2020, 139(3): 455-465.
111	LIU F, HU P, ZHENG B, et al. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images[J]. Agricultural and Forest Meteorology, 2021, 296: ID 108231.
112	HU T, SUN X, SU Y, et al. Development and performance evaluation of a very low-cost UAV-LiDAR system for forestry applications[J]. Remote Sensing, 2020, 13(1): ID 77.
113	LUO S, LIU W, ZHANG Y, et al. Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data[J]. Computers and Electronics in Agriculture, 2021, 182: ID 106005.
114	管贤平, 刘宽, 邱白晶, 等. 基于机载三维激光扫描的大豆冠层几何参数提取[J]. 农业工程学报, 2019, 35(23): 96-103.
114	GUAN X, LIU K, QIU B, et al. Extraction of geometric parameters of soybean canopy by airborne 3D laser scanning[J]. Transactions of the CSAE, 2019, 35(23): 96-103.
115	VIKHE P, VENKATESAN S, CHAVAN A, et al. Mapping of dwarfing gene Rht14 in durum wheat and its effect on seedling vigor, internode length and plant height[J]. The Crop Journal, 2019, 7(2): 187-197.
116	刘永康, 李明军, 李景原, 等. 小麦旗叶直立转披动态过程对其高光效的影响[J]. 科学通报, 2009(15): 2205-2211.
116	LIU Y, LI M, LI J, et al. Dynamic changes in flag leaf angle contribute to high photosynthetic capacity[J]. Chinese Science Bulletin, 2009, 54(15): 2205-2211.
117	CHENG T, LU N, WANG W, et al. Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery[J]. Frontiers in Plant Science, 2019, 10: ID 1601.
118	CHE Y, WANG Q, XIE Z, et al. Estimation of maize plant height and leaf area index dynamic using unmanned aerial vehicle with oblique and nadir photography[J]. Annals of Botany, 2020, 126(4): 765-773.

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