Research Status and Prospect on Height Estimation of Field Crop Using Near-Field Remote Sensing Technology

doi:10.12133/j.smartag.2021.3.1.202102-SA033

Abstract

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

CLC Number:

S127

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.

Figures/Tables 8

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应用	传感器	平台/测量高度	作物	模型	RMSE	R²
生物量估算	激光雷达^[73]	地面固定平台	小麦	幂函数回归模型	1.76 t/ha	0.82
	超声波^[74]	地面固定平台	生菜	指数回归模型	——	0.80
	可见光相机^[75]	无人机/50 m	小麦	偏最小二乘回归模型	0.96 t/ha	0.74
	可见光相机^[76]	无人机/25 m	水稻	随机森林	2.10 t/ha	0.90
	可见光相机^[77]	无人机/44 m	洋葱	作物体积模型	1.53 t/ha	0.95
倒伏监测	激光雷达^[78]	无人机/15 m	玉米	通过株高变化定量测定倒伏程度，株高测量精度R²=0.964，RMSE=0.127 m
	可见光相机^[79]	无人机/20~50 m	玉米	通过设定阈值量化作物倒伏率，与地面实测值相比R²=0.50，RMSE=0.09
	可见光相机^[80]	无人机/35 m	大麦	通过设定阈值量化作物倒伏率，与地面实测值相比，其最佳精度R²=0.96，RMSE=0.08
产量预测	可见光相机^[81]	无人机/50 m	玉米	多元回归模型	0.13 t/ha	0.74
	可见光相机^[82]	无人机/50 m	甘蔗	作物模型	1.09 t/ha	0.44
	可见光相机^[83]	无人机/50 m	棉花	多元回归模型	0.16 t/ha	0.94
	可见光相机^[84]	无人机/30 m	大豆	偏最小二乘回归模型	0.42 t/ha	0.81
	高光谱相机^[63]	无人机/50 m	小麦	偏最小二乘回归模型	0.65 t/ha	0.77
辅助育种	可见光相机^[85]	无人机/30 m	小麦	对株高性状进行全基因组和QTL标记，其预测的基因组值与实际值相关性在0.47~0.53之间
辅助育种	可见光相机^[86]	无人机/40~60 m	玉米	通过对7个与株高相关的性状进行全基因组关联研究，共鉴定出68个QTL，其中35%的QTL与已被报道的控制株高性状的QTL重合

类别	条件				精度		成本
类别	DTM	GCP	地面数据	冠层密度	R2	RMSE	人工成本	时间成本	操作成本
1	√	√		稀疏/密集	★★★★★	★★★★	★★☆	★☆	★★
1	√	√	√	稀疏/密集	★★★★★	★★★★★	☆	☆	☆
2		√		稀疏	★★★★★	★★★★	★★★☆	★★★★	★★★
				密集	★☆	/	★★★☆	★★★★	★★★
			√	稀疏	★★★★★	★★★★★	★☆	★★★	★☆
			√	密集	★☆	★★	★☆	★★★	★☆
3	√			稀疏	★★	/	★★★★	★★☆	★★★★
				密集	★★	/	★	★★	★★
			√	稀疏	★★	★★★	★★★★	★★☆	★★★★
			√	密集	★★	★★★	★	★★	★★
4				稀疏	★	/	★★★★★	★★★★★	★★★★★
				密集	★	/	★★★★★	★★★★★	★★★★★
			√	稀疏	★	★☆	★★★	★★★★	★★★☆
			√	密集	★	★☆	★★★	★★★★	★★★☆

传感器	观测对象	FOV /（°）	测距精度/cm	重量/kg	飞行速度/（m·s^-1）	测量高度/m	点云密度/（pts·m^-2）	精度
Livox MID 40	林木^[112]	38.40	2.00	0.76	4.00	100.00	464.5	R²=0.96，RMSE=0.59 m
RIEGL VUX-1UAV	玉米^[78]	330	0.50	3.50	3.00	15.00	112.0~570.0	R²=0.96，RMSE=0.13 m
	小麦^[26]				5.85	41.84	997.0	R²=0.78，RMSE=0.03 m
	马铃薯^[26]				5.85	41.84	833.0	R²=0.50，RMSE=0.12 m
	甜菜^[26]				5.85	41.84	933.0	R²=0.70，RMSE=0.07 m
	玉米^[113]				—	150.00	420.0	R²=0.65，RMSE=0.24 m
	大豆^[113]				—	150.00	420.0	R²=0.40，RMSE=0.09 m
Velodyne VLP-16	棉花^[4]	360	3.00	0.83	2.00	9.00	1682.0	RE=12.73%，RMSE=0.03 m
Velodyne VLP-16	大豆^[114]	360	3.00	0.83	0.50	9.00	1600.0	RE=5.14%

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