Geometric Parameters Extraction of Tomato Canopy in Greenhouse Based on LiDAR

doi:10.6048/j.issn.1001-4330.2021.10.019

Abstract

Abstract:

【Objective】 To achieve the accurate acquisition of the canopy data of single plant in the greenhouse tomato crop row, to analyze the crop growth status and to provide canopy data support for target spraying. 【Methods】 A 3 d LiDAR was used to build A detection platform for tomato plant canopy. A 3D LiDAR was used to move the 3D LiDAR at A speed of 0.05 m/s. A total of 40 tomato plant point clouds were saved by Ctrlview, the upper computer software of the radar. To bilateral point cloud using ICP (iterative closest point) algorithm for registration, the plane fitting method based on characteristic value is to remove the ground, using the mean shift algorithm (Meanshift) line segmentation of tomato yield point in the cloud, obtain canopy parameters, and comparing with manual measurement verification accuracy, finally will yield point cloud using alpha in MATLAB algorithm based on the shape reconstruction and volume, and compared using convex hull algorithm crops reference. 【Results】 The test results show that the measurement errors of the platform in the forward direction and the vertical direction of the lidar are -2.65% and -3.95% respectively. The average absolute error M 12 was 0.025 m and 0.031 m, respectively, compared with the measured height. The average error of the volume obtained by reconstruction using Alpha Shape algorithm is about 15.3% lower than that of the convex hull algorithm, which is not much different from that obtained manually, and the indexes are good. 【Conclusion】 The mean shift algorithm is adopted to tomato line point cloud segmentation result compared with the artificial measure A, B two groups of root mean square error of the RMSE are 0.039 and 0.043 respectively, using the alpha shape algorithm canopy volume access and reference comparison V_RMSE 0.011,3, shows that the laser radar in crop silhouette information has A certain accuracy and reliability of this method can be used to plant crop canopy of greenhouse environment data acquisition.

Key words: greenhouse; LiDAR; data processing; single plant point cloud; volume

摘要：

【目的】准确获取温室番茄作物行中单株冠层数据,为分析作物生长状态和为对靶喷药提供冠层数据支持。【方法】采用三维激光雷达(LiDAR)搭建番茄植株冠层检测平台,使用导轨以0.05 m/s的速度移动三维激光雷达,利用雷达上位机软件Ctrlview保存双侧扫描的A、B 2组共40株番茄植株点云。双侧点云使用ICP(迭代最近点)算法进行配准,利用基于特征值的平面拟合法去除地面,使用均值漂移算法(Meanshift)分割番茄行中的单株点云,获取冠层参数,与人工测量值比较验证精度,将单株点云在MATLAB中使用alpha shape算法进行重建并进行体积的获取,使用凸包算法作物参考值对比。【结果】该检测平台在激光雷达前进方向与垂直前进方向的测量误差分别为-2.65%、-3.95%;获取到的单株番茄植株高度与人工测量值相比,平均绝对误差分别为0.025和0.031 m;重建后求取的体积与凸包算法相比平均误差下降了约15.3%,与人工获取相比相差不大,各指标良好。【结论】番茄行点云分割结果与人工测量相比A、B 2组的均方根误差RMSE分别为0.039和0.043,冠层体积获取与参考值对比V_RMSE为0.011 3,激光雷达在获取作物外形轮廓信息中具有一定的准确性和可靠性,该方法用于温室环境下单株作物冠层数据的获取。

关键词: 温室, 激光雷达, 数据处理, 单株点云, 体积

CLC Number:

S625
S641.2

Zhenghe YANG, Chen YU, Huimin YANG, Yifei CHEN, Xin ZHOU, Yan MA, Xuenong WANG. Geometric Parameters Extraction of Tomato Canopy in Greenhouse Based on LiDAR[J]. Xinjiang Agricultural Sciences, 2021, 58(10): 1909-1917.

杨征鹤, 喻晨, 杨会民, 陈毅飞, 周欣, 马艳, 王学农. 基于LiDAR的温室番茄冠层几何参数提取[J]. 新疆农业科学, 2021, 58(10): 1909-1917.

Figures/Tables 14

Fig.1 Structure diagram of detection platform Note: 1. Lidar part; 2. Stents part ;3. Guide part

Fig.2 Data processing flow chart

Fig.3 Coordinate system and coordinate parameters

Fig.4 Sample collection scene of greenhouse

Fig.5 Schematic diagram of row point cloud of tomato crops

Fig.6 Schematic diagram of adjacent point clouds

Fig.7 Schematic diagram of the rule framework Note: l1 and l3 are the forward direction of the lidar; l2 and l4 are the direction of vertical lidar

Table 1 Comparison table between the manual measurement value of rule support and the platform detection value

参数 Parameter	人工 Manual (cm)	平台 Platform (cm)	相对误差 Relative error (%)
$l 1$	60.0	58.3	-2.8
$l 2$	80.0	76.4	-4.5
$l 3$	60.0	58.5	-2.5
$l 4$	80.0	77.3	-3.4

Table 1 Comparison table between the manual measurement value of rule support and the platform detection value

参数 Parameter	人工 Manual (cm)	平台 Platform (cm)	相对误差 Relative error (%)
$l 1$	60.0	58.3	-2.8
$l 2$	80.0	76.4	-4.5
$l 3$	60.0	58.5	-2.5
$l 4$	80.0	77.3	-3.4

Table 2 Comparison of canopy height measuredmanually with platform measured values

编号 No.	人工 Manual (m)	平台 Platform (m)	差值 Difference (m)
1	0.70	0.73	-0.03
2	0.73	0.75	-0.02
3	0.59	0.64	-0.05
4	0.50	0.56	-0.06
5	0.66	0.73	-0.07
6	0.75	0.76	-0.01
7	0.55	0.58	-0.03
8	0.45	0.49	-0.04
9	0.57	0.54	0.03
10	0.81	0.84	-0.03
11	0.69	0.74	-0.05
12	0.57	0.58	-0.01
13	0.70	0.70	0.00
14	0.60	0.63	-0.03
15	0.61	0.58	0.03
16	0.49	0.45	0.04
17	0.53	0.59	-0.06
18	0.62	0.65	-0.03
19	0.60	0.65	-0.05
20	0.62	0.65	-0.03
21	1.67	1.68	-0.01
22	1.59	1.63	-0.04
23	1.6	1.71	-0.11
24	1.74	1.76	-0.02
25	1.60	1.58	0.02
26	1.65	1.60	0.05
27	1.45	1.45	0.00
28	1.60	1.64	-0.04
29	1.72	1.75	-0.03
30	1.63	1.64	-0.01
31	1.50	1.56	-0.06
32	1.54	1.56	-0.02
33	1.60	1.65	-0.05
34	1.54	1.57	-0.03
35	1.67	1.69	-0.02
36	1.69	1.65	0.04
37	1.68	1.67	0.01
38	1.70	1.74	-0.04
39	1.68	1.69	-0.01
40	1.74	1.73	0.01

Fig.8 Single tomato point cloud

Fig.9 Linear model of detected and measured values of canopy height

Table 3 Detection value and reference value of tomato canopy volume platform

编号 No.	检测值 Detection value (m³)	参考值 Reference value (m³)	绝对误差 Absolute error (m³)	相对误差 Relative error (%)
1	0.019 0	0.022 3	-0.003 3	14.8
2	0.017 2	0.019 2	-0.002 0	10.4
3	0.022 1	0.025 2	-0.003 1	12.3
4	0.027 6	0.031 7	-0.004 1	12.9
5	0.016 1	0.019 0	-0.002 9	15.3
6	0.020 3	0.024 2	-0.003 9	16.1
7	0.016 6	0.018 9	-0.002 3	12.2
8	0.019 7	0.023 4	-0.003 7	15.8
9	0.023 4	0.026 3	-0.002 9	11.0
10	0.026 9	0.030 6	-0.003 7	12.1
11	0.074 6	0.090 2	-0.015 6	17.3
12	0.073 9	0.087 1	-0.013 2	15.2
13	0.069 2	0.083 1	-0.013 9	16.7
14	0.076 7	0.094 1	-0.017 4	18.5
15	0.081 0	0.094 6	-0.013 6	14.4
16	0.071 4	0.087 4	-0.016 0	18.3
17	0.065 7	0.079 8	-0.014 1	17.7
18	0.069 4	0.087 6	-0.018 2	20.8
19	0.083 2	0.097 6	-0.014 4	14.8
20	0.076 4	0.095 1	-0.018 7	19.7
平均值 Average value			-0.009 4	15.3

Fig.10 Crop canopy reconstruction results

Table 4 Comparison table between manual measurement and platform detection volume

编号 No.	人工 Manual (m³)	平台 Platform (m³)	相对误差 Relative error (%)
1	0.022 6	0.019 0	15.9
2	0.025 9	0.022 1	14.6
3	0.095 2	0.076 7	19.4
4	0.098 6	0.081 0	17.8
5	0.097 8	0.076 4	21.9

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