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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (9): 1816-1823.doi: 10.3724/SP.J.1006.2021.04211

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Estimation of feed rapeseed biomass based on multi-angle oblique imaging technique of unmanned aerial vehicle

ZHANG Jian1(), XIE Tian-Jin1, WEI Xiao-Nan1, WANG Zong-Kai2, LIU Chong-Tao2, ZHOU Guang-Sheng2, WANG Bo2,*()   

  1. 1College of Resources and Environmental Sciences, Huazhong Agricultural University / Macro Agriculture Research Institute, Wuhan 430070, Hubei, China
    2Key Laboratory of Crop Physiology, Ecology and Cultivation (The Middle Reaches of the Yangtze River), Ministry of Agriculture and Rural Affairs / College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
  • Received:2020-09-16 Accepted:2021-01-21 Online:2021-09-12 Published:2021-02-25
  • Contact: WANG Bo E-mail:jz@mail.hzau.edu.cn;wangbo@mail.hzau.edu.cn
  • Supported by:
    National Key Research and Development Program of China “Physiological Basis and Agronomic Management for High-quality and High-yield of Field Cash Crops”(2018YFD1000900);Major Special Projects of Technological Innovation of Hubei Province(2017ABA064)

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Abstract:

To obtain above-ground biomass information quickly and accurately facilitating crop growth monitoring and yield prediction, this study was to evaluate a new method to extract the biomass of feed rapeseed based on UAV with visible-light cameras. The experiment was conducted at the rapeseed experimental base of Huazhong Agricultural University in 2018. To estimate above-ground biomass of rapeseed, a UAV (unmanned aerial vehicle) platform equipped with a five-camera oblique photography system was used to simultaneously obtain images of rapeseed during final flowering period from multiple angles. Three flight altitudes (40, 60, and 80 m) and three seeding densities (3.00×105, 5.25×105, and 7.50×105 plant hm-2) were carried out to assess biomass predictions in a single-camera vertical imaging pattern. Firstly, the rapeseed canopy coverage and plant height information from the image of the UAV were extracted. Secondly, the volume model of rapeseed was obtained by the addition of plant height on the covering area. Finally, a linear regression model was established based on volume model and measured biomass to predict the dry weight of rapeseed. The results were as follows: (1) With the decrease of the flight height of the UAV of the three flight altitudes, the accuracy of biomass prediction was on the rise, and when the flight height was 40 meters, the accuracy of rapeseed biomass estimation was the best (calibration set: r = 0.792, RMSE = 125.0 g m -2, RE = 13.2%; validation set: r = 0.752, RMSE = 139.1 g m -2, RE = 15.3%). (2) When the planting density of rapeseed was higher, the actual biomass was smaller, and the prediction of biomass had a better result by volume model. (3) There was no significant difference in the accuracy of rapeseed biomass estimation between multi-angle imaging and single-camera vertical imaging, both of which had the best results at the flight height of 40 meters with correlation coefficients rof 0.772 and 0.742, respectively. This study indicated that it was feasible to obtain images for extracting rapeseed biomass by a UAV, which could provide the reference for efficient and accurate phenotypic information of field crops.

Key words: unmanned aerial vehicle, biomass, oblique photography, crop volume model, feed rapeseed

Fig. 1

Overview of the experimental areas and UAV platforms A: layout of experimental areas; B: plots and sampling areas; C: oblique photography system with five cameras."

Table 1

Information of CSM and RGB image at three flight heights"

飞行高度
Flight height (m) 		图像数
Number of images 		CSM分辨率
Resolution of CSM
(cm pixel-1) 		可见光影像分辨率
Resolution of RGB image
(cm pixel-1)
40 1020 1.96 0.49
60 698 3.00 0.75
80 536 4.16 1.04

Table 2

Descriptive statistics of rapeseed biomass"

统计量
Statistics 		密度1
Density 1 (g m-2) 		密度2
Density 2 (g m-2) 		密度3
Density 3 (g m-2)
最小值 Min. 516.9 371.3 359.5
最大值 Max. 1230.4 1161.3 1194.2
平均值 Mean 816.6 816.4 761.3
标准差 SD 200.0 163.1 182.7

Fig. 2

Flow chart of biomass acquisition based on VSD method A: the acquisition of crop canopy height; B: the acquisition of crop canopy coverage area; C: the acquisition of volume model; XY: the area of crop canopy; Z: the height of crop canopy."

Table 3

Biomass extraction results at three flight heights"

飞行高度
Flight height (m) 		预测模型
Prediction model 		校正集 Calibration set 验证集 Validation set
相关系数
r 		均方根误差
RMSE
(g m-2) 		相对误差RE
(%) 		相关系数
r 		均方根误差
RMSE
(g m-2) 		相对误差RE
(%)
40 y = 456.962x-17.227 0.792 125.0 13.2 0.752 139.1 15.3
60 y = 443.694x+8.513 0.745 130.2 13.7 0.764 133.9 14.9
80 y = 445.053x-24.698 0.722 138.4 14.4 0.674 152.3 16.7

Fig. 3

Comparison of biomass prediction under different planting densities r: correlation coefficient; RMSE: root mean square error; RE: relative error; D1: 3.00×105 plant hm-2; D2: 5.25×105 plant hm-2; D3: 7.50×105 plant hm-2. "

Table 4

CSM resolutions of the oblique photography system and single vertical camera"

飞行高度
Flight height
(m) 		图像类型 Type of images (cm pixel-1)
倾斜摄影
Oblique photography 		垂直摄影
Vertical photography
40 1.96 1.90
60 3.00 2.92
80 4.16 4.06

Fig. 4

Biomass estimation between oblique photography system and vertical photography CVM = Crop Volume Model; 40 m: the flight height was 40 meters; 60 m: the flight height was 60 meters; 80 m: the flight height was 80 meters. Abbreviations are the same as those given inFig. 3. "

Table 5

Comparison of CSM obtained by the oblique photography system and single vertical camera"

统计量
Statistics (cm) 		飞行高度 Flight height
40 m 60 m 80 m
油菜
Rapeseed 		道路
Road 		土壤
Soil 		油菜
Rapeseed 		道路
Road 		土壤
Soil 		油菜
Rapeseed 		道路
Road 		土壤
Soil
最大值 Maximum 5.68 4.13 0.91 1.34 0.10 -2.53 2.53 0.45 -3.86
最小值 Minimum -14.95 -8.36 -8.38 -17.94 -16.26 -16.93 -8.89 -10.99 -13.89
平均值 Mean 3.38 2.91 4.62 7.88 5.12 9.26 4.94 3.51 8.28
极差 Range 20.63 12.49 9.29 19.28 16.36 14.40 11.42 11.44 10.03
标准差 SD 3.24 2.72 2.25 3.24 2.79 2.83 2.57 2.08 2.05
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