摘要
为挑选产糖量高且适合机械化收获的甜菜根型,该文基于多视角图像序列,构建了207个基因型甜菜根的三维点云模型。基于三维点云提取了描述甜菜根形态特征的10个表型参数:最大直径、根长、凸包体积、顶投影面积、紧凑度、凸起率、凸起角、根头比、根尾比和根体渐细指数。与人工测定的最大直径和根长值进行校验,决定系数R^2均在0.95以上。其中根长、凸包体积及顶投影面积与生产指标呈极显著(P<0.01)相关关系。采用稳定性较高的K-medoids聚类算法将甜菜根型分为4类,结合专家知识获取理想根构型的主要特征为根型中等长度、比例适中。采用线性判别、随机森林、支持向量机、决策树和朴素贝叶斯5种预测模型进行根型判别。结果表明5种根系判别模型预测准确率均在70.0%以上,随机森林判别准确率达到81.4%。研究结果将为培育高品质和适应机械化生产的甜菜品种提供依据。
Sugar beet is one of the main crops for sugar production in the world,and originated from the western and southern coasts of Europe.Selecting and breeding of varieties of sugar beet based on plant phenotyping are the key factors for the development of sugar beet industry on a large-scale cultivation.In China,sugar beet was widely planted in arid and semi-arid regions,particularly for poverty alleviation of farmers living in border areas and ethnic minority areas.The type of beet root with great different genotypes directly determines the sugar yield and mechanization efficiency in modern agriculture.The traditional classification of beet root type depends mainly on manual separation,and thereby greatly limits industry production and breeding of the sugar beet due to heavy workload and relatively large errors.In order to meet the requirements of high-throughput analysis,a three-dimensional(3D)phenotyping technique with multi-view images was recently developed to facilitate the classification of fruit and vegetable with high accuracy and efficiency.In this study,the beet roots with 207 genotypes were selected as experimental materials.Multi-view images were obtained by moving mobile phone around beet root.Three-dimensional point clouds were reconstructed in 3DF Zephyr Aerial software,which can restore position and direction from a dataset of multi-views images to extract for the matching feature points between each pair of images.After the postprocessing of the matching images,including noise reduction,rotating and segment,the detailed features of beet root shape,color,and texture can be achieved in the 3D point cloud.Ten phenotypic parameters can be used to clarify the morphological characteristics of beet roots,the maximum diameter,root length,convex hull volume,top projection area,compactness,convex index,convex angle,distal root end ratio,proximal root end ratio and root taper index.There was a good agreement between the measured maximum diameter and root length,with coefficient of determination R^2>0.95.The K-
作者
柴宏红
邵科
于超
邵金旺
王瑞利
随洋
白凯
刘云玲
马韫韬
Chai Honghong;Shao Ke;Yu Chao;Shao Jinwang;Wang Ruili;Sui Yang;Bai Kai;Liu Yunling;Ma Yuntao(North China Key Laboratory of Arable Land Conservation,Ministry of Agriculture,College of Land Science and Technology,China Agricultural University,Beijing 100193,China;Inner Mongolia Key Laboratory of Molecular Biology of Characteristic Plants,Inner Mongolia Institute of Biotechnology,Huhhot 010070,China;Hulunbuir Ecological Environment Monitoring Station of the Inner Mongolia,Hulunbuir 021008,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第10期181-188,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
内蒙古自治区科技重大专项和科技成果重大转化项目。
关键词
图像处理
机器学习
三维点云
甜菜
根型
表型
分类
image procession
machine learning
three dimensional point cloud
beet
root type
phenotype
classification
