番茄植株生长过程中茎叶表型的无损分割与提取

doi:10.13304/j.nykjdb.2024.0080

摘要/Abstract

摘要：

针对当前番茄植株表型参数难以无损提取的问题，提出基于三维重建的番茄植株茎叶分割与表型提取方法。首先采集番茄植株的多视角图像序列构建植株三维模型，采用多种滤波算法结合的方式完成预处理；对预处理后的点云使用基于拉普拉斯的骨架提取算法提取骨架，以植株骨架为基础完成茎秆与叶片的分割，使用基于超体素聚类的方法分割单叶；提取株高、茎粗、叶倾角、叶长、叶宽和叶面积共6个表型参数。结果表明，茎叶分割的平均准确率、平均召回率和平均F1分数分别为0.88、0.80和0.84，分割指标均优于其他4种分割算法；株高、茎粗、叶倾角、叶长、叶宽和叶面积计算值与实测值之间的决定系数分别为0.97、0.84、0.88、0.94、0.92和0.93，均方根误差分别为2.17 cm、0.346 cm、5.65°、3.18 cm、2.99 cm和8.79 cm²，该方法的计算值与实测值具有较强的相关性，研究结果为番茄植株的高通量表型参数提取提供了技术支持。

关键词: 番茄, 三维重建, 茎叶分割, 表型提取

Abstract:

Aiming at the current problem that it is difficult to extract the phenotypic parameters of tomato plants without any loss， the stem and leaf segmentation and phenotypic extraction method of tomato plants based on 3D reconstruction was proposed. Firstly， the multi-view image sequences of tomato plants were collected to construct a 3D model of the plant， and a combination of multiple filtering algorithms was used to complete the pre-processing. The skeleton of the pre-processed point cloud was extracted using the Laplace-based skeleton extraction algorithm， and the segmentation of the stem and leaves was completed based on the skeleton of the plant， and the segmentation of the single leaves was completed using the method based on the clustering of hyperbolomers. 6 phenotypic parameters including the height of the plant， the thickness of the stem， the angle of the inclination of the leaf， the length of the leaf， the width of the leaf， and the area of the leaf were also extracted. The results showed that the average accuracy， average recall and average F1 score of stem and leaf segmentation were 0.88， 0.80 and 0.84， respectively， and the segmentation indexes were better than the other 4 segmentation algorithms. The coefficients of determination between the calculated and measured values of plant height， stem thickness， leaf inclination， leaf length， leaf width and leaf area were 0.97， 0.84， 0.88， 0.94， 0.92 and 0.93， respectively， and the root mean square errors were 2.17 cm， 0.346 cm， 5.65°， 3.18 cm， 2.99 cm and 8.79 cm². The measured values of the proposed method had a strong correlation with the calculated values， which provided technical support for high-throughput phenotypic parameter extraction in tomato plants.

Key words: tomato, three-dimensional reconstruction, stem and leaf segmentation, phenotype extraction

中图分类号:

S126

王亚鑫, 吕洋成, 王文琦, 刘琦, 杨杰, 任桂鸿, 张吴平, 李富忠. 番茄植株生长过程中茎叶表型的无损分割与提取[J]. 中国农业科技导报, 2025, 27(7): 90-100.

Yaxin WANG, Yangcheng LYU, Wenqi WANG, Qi LIU, Jie YANG, Guihong REN, Wuping ZHANG, Fuzhong LI. Nondestructive Segmentation and Extraction of Stem and Leaf Phenotypes During Tomato Plant Growth[J]. Journal of Agricultural Science and Technology, 2025, 27(7): 90-100.

图/表 14

图1 图像采集方式

Fig. 1 Image acquisition method

图2 番茄植株点云重建及预处理A：稠密点云；B：点云去噪；C：点云精简；D：番茄植株动态生长重建情况

Fig. 2 Reconstruction and preprocessing of tomato plant point cloudA： Dense point cloud； B：Point cloud denoising； C：Point cloud reduction； D：Dynamic growth and reconstruction of tomato plants

图3 番茄骨架提取A：收缩后骨架点；B：点云骨架无向图

Fig. 3 Tomato skeleton extractionA： Skeleton points after contraction； B：Undirected graph point cloud skeleton

图4 茎秆提取示意A：坐标系转换；B：提取的骨架最高点路径；C：高度约束；D：半径约束；E：分离后的茎杆与叶片

Fig. 4 Schematic diagram of stem extractionA： Coordinate system conversion； B：Extract the highest point path of the skeleton； C：Height constraint； D：Radius constraint； E：Separated stem and leaves

图5 单叶分割A：叶片点云；B：叶片超体素；C：叶片分割情况

Fig. 5 Single leaf segmentationA： Leaf point cloud； B：Leaf supervoxels； C：Leaf segmentation situation

图6 表型参数测量A：株高；B：茎粗；C：叶倾角；D：叶片点云；E：叶长和叶宽；F：叶面积

Fig. 6 Phenotype parameter measurementA： Plant height； B：Stem thickness； C：Leaf inclination； D：Leaf point cloud； E：Leaf length and leaf width； F：Leaf area

表1 不同参数对叶片分割影响统计

Table 1 Statistics on the effect of different parameters on blade segmentation

参数 Parameter			超体素块数量 Number of superstitial blocks	运行时间 Running time/s
种子分辨率 $R s e e d$	体素分辨率 $R v o x e l$	最小分割尺寸 $S m i n$	超体素块数量 Number of superstitial blocks	运行时间 Running time/s
0.05	0.01	0.10	378	21.23
0.05	0.05	0.20	386	20.79
0.10	0.01	0.10	291	18.44
0.10	0.05	0.20	274	18.03
0.15	0.05	0.20	191	15.97
0.25	0.10	0.30	123	14.22
0.25	0.15	0.40	108	13.76
0.35	0.10	0.35	56	10.17
0.45	0.10	0.30	19	7.66
0.45	0.15	0.40	10	7.31

表1 不同参数对叶片分割影响统计

Table 1 Statistics on the effect of different parameters on blade segmentation

参数 Parameter			超体素块数量 Number of superstitial blocks	运行时间 Running time/s
种子分辨率 $R s e e d$	体素分辨率 $R v o x e l$	最小分割尺寸 $S m i n$	超体素块数量 Number of superstitial blocks	运行时间 Running time/s
0.05	0.01	0.10	378	21.23
0.05	0.05	0.20	386	20.79
0.10	0.01	0.10	291	18.44
0.10	0.05	0.20	274	18.03
0.15	0.05	0.20	191	15.97
0.25	0.10	0.30	123	14.22
0.25	0.15	0.40	108	13.76
0.35	0.10	0.35	56	10.17
0.45	0.10	0.30	19	7.66
0.45	0.15	0.40	10	7.31

图7 不同算法参数的叶片分割结果A：参数设置过小；B：参数设置过大；C：参数设置适中

Fig. 7 Leaf segmentation results with different algorithm parametersA：Parameter setting too small； B：Parameter setting too large； C：Moderate parameter setting

图8 不同生长天数的番茄茎叶分割结果A：手动分割真值；B：算法分割结果。黑色圈代表算法分割错误部分

Fig. 8 Tomato stem and leaf segmentation results at different growth daysA：True value of manual segmentation； B：Algprithm segmentation results. The black circle represents the part of the algorithm that is segmented incorrectly

表2 不同生长天数番茄的茎叶分割精度

Table 2 Precision of stem and leaf segmentation in tomato on different growth days

定植天数 Growth days/d	准确率 Precision	召回率 Recall	F₁分数 F₁-score
5	0.88	0.80	0.84
14	0.91	0.84	0.87
21	0.92	0.85	0.88
30	0.88	0.82	0.85
45	0.86	0.77	0.81
60	0.84	0.74	0.79
平均 Average	0.88	0.80	0.84

图9 不同算法的分割效果A：本研究方法；B：基于骨架的提取方法；C：法线微分差异法；D：区域生长分割法；E：基于凹凸性的分割方法

Fig. 9 Segmentation effects of different algorithmsA： This research method； B：A method based on skeleton extraction； C：Normal differential method； D：Regional growth segmentation method；E：Segmentation method based on concavity and convexity

表3 5种方法茎叶分割精度对比

Table 3 Comparison of stem and leaf segmentation accuracy among five methods

方法 Method	平均准确率 Average of precision	平均召回率 Average of recall	平均F₁分数 Average of F₁-score
本研究方法 This research method	0.88	0.80	0.84
基于骨架提取的分割方法 Segmentation method based on skeleton extraction	0.77	0.63	0.76
法线微分差异法 Normal differential method	0.64	0.59	0.57
区域生长分割法 Regional growth segmentation method	0.58	0.53	0.59
基于凹凸性的分割方法 Segmentation method based on concavity and convexity	0.54	0.57	0.62

图10 表型参数测量结果A：株高；B：茎粗；C：叶倾角；D：叶长；E：叶宽；F：叶面积

Fig. 10 Phenotypic parameter measurement resultsA： Plant height； B：Stem thickness； C：Leaf inclination； D：Leaf length； E：Leaf width； F：Leaf area

表4 算法效率分析

Table 4 Algorithm efficiency statistics

指标Index

点云重建

Point cloud reconstruction

茎叶分割

Stem and leaf segmentation

表型提取

Phenotypic extraction

总计

Total

最小处理时间Minimum processing time/min

最大处理时间Maximum processing time/min

平均处理时间Average processing time/min

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