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作物学报

作物学报 ›› 2021, Vol. 47 ›› Issue (9): 1816-1823.doi: 10.3724/SP.J.1006.2021.04211

• 研究简报 • 上一篇    下一篇

无人机多角度成像方式的饲料油菜生物量估算研究

张建1(), 谢田晋1, 尉晓楠1, 王宗铠2, 刘崇涛2, 周广生2, 汪波2,*()   

  1. 1华中农业大学资源与环境学院 / 宏观农业研究院, 湖北武汉 430070
    2华中农业大学植物科学技术学院 / 农业农村部长江中游作物生理生态与耕作重点实验室, 湖北武汉 430070
  • 收稿日期:2020-09-16 接受日期:2021-01-21 出版日期:2021-09-12 网络出版日期:2021-02-25
  • 通讯作者: 汪波
  • 作者简介:E-mail: jz@mail.hzau.edu.cn
  • 基金资助:
    国家重点研发计划项目“大田经济作物优质丰产的生理基础与调控”(2018YFD1000900);湖北省技术创新专项重大项目(2017ABA064)

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 Published:2021-09-12 Published online:2021-02-25
  • Contact: WANG Bo
  • 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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摘要：

旨在探索并评估一种通过无人机平台搭载可见光相机提取饲料油菜生物量的新方法。试验于2018年在华中农业大学油菜试验基地展开, 利用无人机搭载五相机倾斜摄影系统同时从多个角度获取油菜终花期的可见光图像, 试验共设置3种无人机飞行高度(40、60和80 m)和3种播种密度(3.00×105、5.25×105和7.50×105株 hm-2), 并评估和对比了多角度和单相机垂直2种成像方式的生物量预测结果。试验首先通过无人机图像提取油菜冠层覆盖度和株高信息; 然后通过株高在覆盖面积上进行累加获得作物体积模型; 最后基于作物体积模型与实测生物量建立线性回归模型预测油菜干物质重量。结果表明, (1) 在本试验设置的3个飞行高度中, 随着无人机飞行高度下降, 生物量预测精度呈上升趋势, 其中飞行高度为40 m时, 油菜生物量估算精度最佳(校正集: r = 0.792, RMSE = 125.0 g m -2, RE = 13.2%; 验证集: r = 0.752, RMSE = 139.1 g m -2, RE = 15.3%)。(2) 种植密度越高, 其实际生物量越小, 通过作物体积模型预测生物量的效果更好。(3) 多角度成像方式与单相机垂直成像方式在油菜生物量估测精度上没有显著差异, 两者皆在40 m高度下具有最好的生物量预测效果, 相关系数r分别为0.772和0.742。以上结果表明, 基于无人机低成本可见光成像建模技术提取饲料油菜生物量是可行的, 本研究可为大田作物地上生物量信息的无损高效监测提供易于实施的解决方案和技术参考。

关键词: 无人机, 生物量, 倾斜摄影, 作物体积模型, 饲料油菜

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

图1

试验区与无人机平台概况 A: 试验区布置; B: 小区面积和采样区域; C: 五相机倾斜摄影系统。"

表1

3个飞行高度下的CSM和可见光影像信息"

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

表2

油菜生物量统计特征值"

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

图2

基于VSD法获取生物量的流程图 A: 作物冠层高度获取; B: 作物冠层覆盖面积获取; C: 体积模型获取; XY: 作物冠层覆盖面积; Z: 作物冠层高度。"

表3

3种飞行高度下的生物量提取结果"

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

图3

不同种植密度下生物量预测的结果比较 r: 相关系数; RMSE: 均方根误差; RE: 相对误差; D1: 3.00×105株 hm-2; D2: 5.25×105株 hm-2; D3: 7.50×105株 hm-2。 "

表4

五相机倾斜摄影系统图像与单个垂视相机CSM的分辨率"

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

图4

倾斜摄影系统与垂直摄影估算生物量的结果 CVM = 作物体积模型; 40 m: 无人机飞行高度为40 m; 60 m: 无人机飞行高度为60 m; 80 m: 无人机飞行高度为80 m。缩写同图3。 "

表5

五相机倾斜摄影系统与单个垂视相机获取的CSM的差异比较"

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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