摘要
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。
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.
作者
贾奥博
董天浩
张彦
朱冰琳
孙延国
吴元华
石屹
马韫韬
郭焱
JIA Aobo;DONG Tianhao;ZHANG Yan;ZHU Binglin;SUN Yanguo;WU Yuanhua;SHI Yi;MA Yuntao;GUO Yan(College of Land Science and Technology,China Agricultural University,Beijing 100193,China;Institute of Tobacco Research,Chinese Academy of Agricultural Sciences,Qingdao 266101,Shandong,China)
出处
《浙江大学学报（农业与生命科学版）》
CAS
CSCD
北大核心
2022年第3期393-402,共10页
Journal of Zhejiang University:Agriculture and Life Sciences
基金
中国烟草总公司山东省公司重点项目“山东‘中棵烟’特征及形成机制研究”(201803)。
关键词
三维点云
烟草
表型
机器学习
株型
three-dimensional point cloud
tobacco
phenotype
machine learning
plant type
