Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning

doi:10.3785/j.issn.1008-9209.2021.05.173

Journal of Zhejiang University (Agriculture and Life Sciences)

2022, Vol. 48

Issue (3): 393-402 DOI: 10.3785/j.issn.1008-9209.2021.05.173

Agricultural engineering

Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning

Aobo JIA¹(

),Tianhao DONG¹,Yan ZHANG²,Binglin ZHU¹,Yanguo SUN²,Yuanhua WU²,Yi SHI²,Yuntao MA¹,Yan GUO¹(

)

^1.College of Land Science and Technology, China Agricultural University, Beijing 100193, China
^2.Institute of Tobacco Research, Chinese Academy of Agricultural Sciences, Qingdao 266101, Shandong, China

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Abstract

To develop an efficient method for quantifying tobacco plant types in the field, the three-dimensional (3D) point clouds of individual plant of five tobacco cultivars were reconstructed based on multi-view image sequences using the structure from motion method. According to the plant type characteristic indexes commonly used, ten phenotypic parameters such as plant height, top width, bottom width, and maximum width of leaf layer were automatically extracted based on the 3D point cloud of tobacco plant, and the calculation accuracy was evaluated based on the plant height and maximum width of leaf layer measured manually in situ in the field. The results indicated the coefficients of determination (R²) of the plant height and maximum width of leaf layer extracted from the 3D point cloud were all greater than 0.97, and the root mean square errors were 3.0, 3.1 cm, respectively. Meanwhile, the extracted phenotypic parameters of tobacco plants were analyzed by different methods. The results of intergroup correlation analysis showed that 16 pairs of traits were extremely significant positive correlations, while one pair of traits was extremely significant negative correlation. The results of one-way multivariate analysis of variance showed that there were highly significant differences among the plant types. The first three principal components were extracted by principal component analysis, and their cumulative contribution rate to the overall variance was 81.6%. The accuracy of plant type discrimination was 93.7% using Stacking ensemble learning method, which was significantly higher than those using random forest, support vector machine and naive Bayesian. This study can provide a method basis for phenotypic characteristics and plant type recognition of field-grown tobacco plants.

Key words： three-dimensional point cloud tobacco phenotype machine learning plant type

Received: 17 May 2021 Published: 07 July 2022

CLC:

TP 391.7

Corresponding Authors: Yan GUO E-mail: aobo.jia@cau.edu.cn;yan.guo@cau.edu.cn

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	Aobo JIA
	Tianhao DONG
	Yan ZHANG
	Binglin ZHU
	Yanguo SUN
	Yuanhua WU
	Yi SHI
	Yuntao MA
	Yan GUO

Cite this article:

Aobo JIA,Tianhao DONG,Yan ZHANG,Binglin ZHU,Yanguo SUN,Yuanhua WU,Yi SHI,Yuntao MA,Yan GUO. Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning. Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48(3): 393-402.

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https://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2021.05.173 OR https://www.zjujournals.com/agr/Y2022/V48/I3/393

基于三维点云和集成学习的大田烟草株型特征识别

为构建高效的大田烟草株型定量化方法，本研究基于多视角图像序列并采用运动恢复结构算法重建了5个品种烟草植株的三维点云。根据常用的烟草株型特征指标，基于烟株三维点云自动提取株高、顶宽、底宽、叶层最大宽等10个表型参数，并基于大田原位手动测量的株高和叶层最大宽对计算精度进行评估。结果表明，基于三维点云提取的株高和叶层最大宽与实测值的决定系数（R²）均大于0.97，均方根误差分别为3.0、3.1 cm。同时，采用不同方法对提取的烟草表型性状进行分析。组间相关性分析结果表明，有16对性状呈极显著正相关，1对性状呈极显著负相关。单因素多元方差分析结果表明，各品种株型之间具有极显著差异。利用主成分分析提取前3个主成分，其对总体方差的累计贡献率为81.6%。基于Stacking集成学习方法进行株型判别，其准确率达到93.7%，显著高于随机森林、支持向量机和朴素贝叶斯等3种机器学习模型的准确率。本研究可为大田烟草表型特征及株型识别提供方法依据。

关键词： 三维点云, 烟草, 表型, 机器学习, 株型

Fig. 1 Tobacco point cloud reconstruction and pre-processing based on image sequences

Table 1 Extracted phenotypic parameters of tobacco

Fig. 2 Schematic diagram of each phenotypic parameter extraction of tobaccoA. Front view of tobacco plant; B. Minimum enclosing box volume and convex hull volume; C. Top view of tobacco plant; D. Stem and leaf angle. Please see the Table 1 for the details of each phenotypic parameter, and the same as below.

Fig. 3 Ensemble learning method based on Stacking

Fig. 4 Canopy point cloud of five tobacco cultivars based on multi-view image sequence reconstructionHH: Honghuadajinyuan; XK: New K326; LY: Liaoyan No. 1;QG: Qinggeng; EMS: EMS mutant. The same as below.

Fig. 5 Comparisons of plant height and maximum width of leaf layer calculated based on reconstructed point cloud of tobacco canopy with the measured valuesn: Number of samples.

Fig. 6 Correlation analysis of phenotypic parametersSingle asterisk (*) and double asterisks (**) mean significant and extremely significant correlations at the 0.05 and 0.01 probability levels, respectively.

Fig. 7 Contribution rates of tobacco phenotypes to each principal component

Fig. 8 Comparisons of differences among phenotypes of tobacco cultivarsDifferent lowercase letters above bars indicate significant differences at the 0.05 probability level.

Table 2 Comparisons of plant type discrimination accuracy and evaluation indexes of different models


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