Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (16): 2776-2786.doi: 10.3864/j.issn.0578-1752.2019.16.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Multi-View Geometric Reconstruction of Plant Based on Improved Region-Growing Algorithm

XIAO ShunFu,LIU ShengPing(),LI ShiJuan,DU MingZhu,Lü ChunYang,LIU DaZhong,YANG FeiFei,LIU Hang   

  1. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081
  • Received:2019-03-30 Accepted:2019-05-15 Online:2019-08-16 Published:2019-08-21
  • Contact: ShengPing LIU E-mail:liushengping@caas.cn

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Abstract:

【Objective】 Three-dimensional reconstruction of three kinds of plants canopy with different levels of complexity was studied to provide a new method for more accurate acquisition of plants canopy phenotypic characteristics. 【Method】 In this paper, a sequence of pictures of three kinds of plant canopy with different levels of complexity were gotten by using a SLR camera, and dense point clouds of plants were gotten by using three-dimensional reconstruction method. Then, the original scale of dense point clouds of plants was restored, and noisy point of dense point clouds and segmenting plants canopy were filtered by using improved region-growing algorithm with the help of laser scanner, then the accuracy of leaves reconstructed from multi-view geometry method was evaluated from two-dimensional and three-dimensional aspects by manual measurement and laser scanning. Two-dimensional accuracy evaluation was carried out by statistical analysis of actual measured values of leaves length and width and comparing with leaves length and width reconstructed by multi-view geometry method and leaves length and width obtained by laser scanner, respectively. Three-dimensional accuracy was evaluated by using the traditional mesh comparison method Hausdorff distance and Geomagic Qualify software, which was a better accurate 3D accuracy comparison software in industrial meshes comparing. 【Result】 The results showed that the judgment coefficients (R 2) between the phenotypic information of plants leaves and manual measurements were higher than 0.96. The judgment coefficients (R 2) between the phenotypic information of plant leaves obtained by laser scanning and manual measurements were higher than 0.99. The proportion of leaves obtained by multi-view geometric reconstruction and laser scanning was more than 97% in the deviation range of 0-±1 mm. Taking the leaves mesh scanned by laser as a reference, more than 90% of the Hausdorff distance of the leaves mesh reconstructed by multi-view geometry was between 0-2 mm. It was proved that the combination of the multi-view geometric reconstruction method with the improved region-growing algorithm could achieve ideal reconstruction results for plants with different complexity. 【Conclusion】The reconstruction method combining multi-view geometry method with the improved region-growing algorithm proposed in this paper could make up for the shortcomings of region-growing algorithm. It had better segmentation effect on the surface of uneven plants canopy, and was suitable for three-dimensional reconstruction of plants with different complexity. It could provide some reference for breeding research to obtain plant phenotypes.

Key words: multi-view geometry, three-dimensional reconstruction, plant, region-growing algorithm, digital camera

Fig. 1

Workflow of three-dimensional reconstruction and the accuracy evaluation"

Fig. 2

Camera position reference"

Fig. 3

Workflow of generating point clouds based on multi-view geometry"

Fig. 4

Segmentation result of pepper by using region-growing algorithms with different parameters"

Fig. 5

The comparison of results using the region-growing algorithm and its improved algorithm"

Fig. 6

Results of point clouds processing and 3D reconstruction of plants"

Fig. 7

The relationship between leaf position and leaf width and length of pepper, cucumber and tomato"

Table 1

Comparisons of leaf length and leaf width between multi-view geometry reconstruction, laser scanning and manual measurement"

植株
Plant		 叶片参数
Leaf parameter		 决定系数
R12		 均方根误差
RMSE1 (mm)		 决定系数
R22		 均方根误差
RMSE2 (mm)
辣椒
Pepper		 长度 Length 0.997 0.49 0.98 0.96
宽度 Width 0.998 0.23 0.995 0.63
黄瓜
Cucumber		 长度 Length 0.998 0.55 0.98 1.57
宽度 Width 0.999 0.42 0.99 1.56
番茄
Tomato		 长度 Length 0.996 0.38 0.96 1.21
宽度 Width 0.992 0.46 0.96 0.93

Table 2

Percentage of intervals of Hausdorff distance calculated from multi-view geometry method and laser scanning (%)"

植株
Plant		 豪斯多夫距离区间 Intervals of Hausdorff distance (mm)
0-2 0-4 4-6 6-8 8-10
辣椒-1 Pepper-1 96.60 99.46 0.42 0.08 0.04
辣椒-2 Pepper-2 95.22 98.30 1.43 0.21 0.06
黄瓜-1 Cucumber-1 91.68 98.21 1.34 0.43 0.02
黄瓜-2 Cucumber-2 90.24 98.53 0.81 0.61 0.05
番茄-1 Tomato-1 93.86 99.11 0.73 0.09 0.07
番茄-2 Tomato-2 91.53 98.89 1.07 0.03 0.01

Table 3

Percentage of deviation calculated from multi-view geometry method and laser scanning"

植株
Plant		 偏差 Deviation (mm) 标准偏差
Standard deviation (mm)
0-±0.5 0-±1 ±1-±1.5 ±1.5-±2 ±2-±2.5
辣椒-1 Pepper-1 94.90 99.46 0.54 0 0 0.24
辣椒-2 Pepper-2 90.22 97.55 2.3 0.15 0 0.30
黄瓜-1 Cucumber-1 63.34 86.58 8 3.57 1.85 0.72
黄瓜-2 Cucumber-2 62.64 83.91 8.44 4.70 2.95 0.70
番茄-1 Tomato-1 84.60 98.06 1.92 0.02 0 0.37
番茄-2 Tomato-2 82.15 97.00 2.79 0.21 0 0.38

Fig. 8

Visualization of reconstruction quality for plants based on deviation"

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