| 基于三维点云的甜菜根表型参数提取与根型判别 |
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| 引用本文： | 柴宏红,邵科,于超,邵金旺,王瑞利,随洋,白凯,刘云玲,马韫韬.基于三维点云的甜菜根表型参数提取与根型判别[J].农业工程学报,2020,36(10):181-188. |
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| 作者姓名： | 柴宏红 邵科 于超 邵金旺 王瑞利 随洋 白凯 刘云玲 马韫韬 |
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| 作者单位： | 中国农业大学土地科学与技术学院农业部华北耕地保育重点实验室,北京 100193;内蒙古自治区特色植物分子生物学重点实验室,内蒙古自治区生物技术研究院,呼和浩特 010070;内蒙古自治区呼伦贝尔生态环境监测站,呼伦贝尔 021008 |
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| 基金项目： | 内蒙古自治区重大专项和科技成果转化项目 |
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| 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。
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| 关 键 词： | 图像处理 机器学习 三维点云 甜菜 根型 表型 分类 |
| 收稿时间： | 2020/2/15 0:00:00 |
| 修稿时间： | 2020/4/21 0:00:00 |
Extraction of phenotypic parameters and discrimination of beet root types based on 3D point cloud |
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| Chai Honghong,Shao Ke,Yu Chao,Shao Jinwang,Wang Ruili,Sui Yang,Bai Kai,Liu Yunling,Ma Yuntao.Extraction of phenotypic parameters and discrimination of beet root types based on 3D point cloud[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(10):181-188. |
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| Authors: | Chai Honghong Shao Ke Yu Chao Shao Jinwang Wang Ruili Sui Yang Bai Kai Liu Yunling Ma Yuntao |
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| Institution: | 1. North China Key Laboratory of Arable Land Conservation, Ministry of Agriculture, College of Land Science and Technology, China Agricultural University, Beijing 100193, China;;2. Inner Mongolia Key Laboratory of Molecular Biology of Characteristic Plants, Inner Mongolia Institute of Biotechnology, Huhhot 010070, China;;3. Hulunbuir Ecological Environment Monitoring Station of the Inner Mongolia, Hulunbuir 021008, China; |
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| Abstract: | 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 R2 > 0.95. The K-medoids clustering algorithm with high stability was selected to classify the beet root into four groups. Group 1, namely as cone beet root, indicates that the maximum root diameter located at the middle of the root body. Group 2, namely as hammer beet root, shows the shortest body of root, the smallest root head ration while larger root tail ration. Group 3, namely as wedge beet root, has the maximum diameter of root body close to the root head, whereas, the width of root from head to tail gradually decreased. Group 4, namely as long wedge beet root, has longer root body than that in group 3, wider root head and smaller root tail. The reduction rate of root body from head to tail was the greatest. Based on the combination of phenotypic traits and experts'' knowledge, Group 1 (cone beet root) and Group 3 (wedge beet root) were recommended due to their high sugar yield, medium root length and moderate proportion. After adjusting the categories by the experts as the true values, five prediction models were established to discriminate beet root type, including linear discrimination, random forest, support vector machine, decision tree, and naive Bayes. The results showed that the prediction accuracies of the five models were above 70.0%, where accuracy of random forest reached 81.4%. These results demonstrated that 3D point cloud reconstructed by multi-view image sequences can be used for the identification of beet root shape, and thereby to effectively improve the yield prediction of sugar beet and the selection of high-quality beet varieties. Since 207 genotypes have been selected for the classification of root types during this time, much more genotypes at different environments can be expected to enrich the 3D phenotyping library, and thereby further improve the accuracy of classification. This finding can provide a potential practical basis for the beet root type screening and breeding. |
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| Keywords: | image procession machine learning three dimensional point cloud beet root type phenotype classification |
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