Maize Stem Contour Extraction and Diameter Measurement Based on Adaptive Threshold Segmentation in Field Conditions
文献类型: 外文期刊
作者: Zhou, Jing 1 ; Wu, Yushan 1 ; Chen, Jian 2 ; Cui, Mingren 1 ; Gao, Yudi 1 ; Meng, Keying 1 ; Wu, Min 1 ; Guo, Xinyu 3 ; Wen, Weiliang 3 ;
作者机构: 1.Jilin Agr Univ, Coll Informat Technol, Changchun 130118, Peoples R China
2.Changchun Elringklinger Ltd, Changchun 130033, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China
关键词: maize; contour extraction; stem diameter; Otsu
期刊名称:AGRICULTURE-BASEL ( 影响因子:3.6; 五年影响因子:3.6 )
ISSN:
年卷期: 2023 年 13 卷 3 期
页码:
收录情况: SCI
摘要: Solving the problem of the stem contour extraction of maize is difficult under open field conditions, and the stem diameter cannot be measured quickly and nondestructively. In this paper, maize at the small and large bell stages was the object of study. An adaptive threshold segmentation algorithm based on the color space model was proposed to obtain the stem contour and stem diameter of maize in the field. Firstly, 2D images of the maize stem in the field were captured with an RGB-D camera. Then, the images were processed by hue saturation value (HSV) color space. Next, the stem contour of the maize was extracted by maximum between-class variance (Otsu). Finally, the reference method was used to obtain the stem diameter of the maize. Scatter plots and Dice coefficients were used to compare the contour extraction effects of the HSV + fixed threshold algorithm, the HSV + Otsu algorithm, and the HSV + K-means algorithm. The results showed that the HSV + Otsu algorithm is the optimal choice for extracting the maize stem contour. The mean absolute error, mean absolute percentage error (MAPE), and root mean square error (RMSE) of the maize stem diameter at the small bell stage were 4.30 mm, 10.76%, and 5.29 mm, respectively. The mean absolute error, MAPE, and RMSE of the stem diameter of the maize at the large bell stage were 4.78 mm, 12.82%, and 5.48 mm, respectively. The MAPE was within 10-20%. The results showed that the HSV + Otsu algorithm could meet the requirements for stem diameter measurement and provide a reference for the acquisition of maize phenotypic parameters in the field. In the meantime, the acquisition of maize phenotypic parameters under open field conditions provides technical and data support for precision farming and plant breeding.
- 相关文献
作者其他论文 更多>>
-
LettuceP3D: A tool for analysing 3D phenotypes of individual lettuce plants
作者:Ge, Xiaofen;Guo, Xinyu;Ge, Xiaofen;Wu, Sheng;Wen, Weiliang;Xiao, Pengliang;Lu, Xianju;Liu, Haishen;Zhang, Minggang;Guo, Xinyu;Ge, Xiaofen;Wu, Sheng;Wen, Weiliang;Xiao, Pengliang;Lu, Xianju;Liu, Haishen;Zhang, Minggang;Guo, Xinyu;Wu, Sheng;Wen, Weiliang;Shen, Fei
关键词:Lettuce; Point cloud segmentation; Deep learning; Phenotypic analysis algorithm
-
3D time-series phenotyping of lettuce in greenhouses
作者:Ma, Hanyu;Wen, Weiliang;Gou, Wenbo;Fan, Jiangchuan;Gu, Shenghao;Guo, Xinyu;Ma, Hanyu;Wen, Weiliang;Gou, Wenbo;Lu, Xianju;Fan, Jiangchuan;Zhang, Minggang;Liang, Yuqiang;Gu, Shenghao;Guo, Xinyu
关键词:Time-series; 3D phenotyping; Rail-driven phenotyping platform; Lettuce; Greenhouse
-
Comprehensive review on 3D point cloud segmentation in plants
作者:Song, Hongli;Wen, Weiliang;Wu, Sheng;Guo, Xinyu;Song, Hongli;Wen, Weiliang;Wu, Sheng;Guo, Xinyu;Song, Hongli
关键词:Plant; Three-dimensional; Point cloud; Segmentation; Multi-scale; Deep learning
-
Revolutionizing Crop Breeding: Next-Generation Artificial Intelligence and Big Data-Driven Intelligent Design
作者:Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhang, Ying;Guo, Xinyu;Zhao, Chunjiang;Huang, Guanmin;Lu, Xianju;Wang, Yanru;Wang, Chuanyu;Zhao, Yanxin
关键词:Crop breeding; Next-generation artificial intelligence; Multiomics big data; Intelligent design breeding
-
Three-Dimensional Time-Series Monitoring of Maize Canopy Structure Using Rail-Driven Plant Phenotyping Platform in Field
作者:Ma, Hanyu;Zhang, Dongsheng;Wen, Weiliang;Fan, Jiangchuan;Gu, Shenghao;Guo, Xinyu;Wen, Weiliang;Gou, Wenbo;Liang, Yuqiang;Zhang, Minggang;Fan, Jiangchuan;Gu, Shenghao;Guo, Xinyu
关键词:maize canopy; time-series phenotype; 3D point cloud; plot segmentation; marginal effect
-
Water phase distribution and its dependence on internal structure in soaking maize kernels: a study using low-field nuclear magnetic resonance and X-ray micro-computed tomography
作者:Wang, Baiyan;Zhao, Chunjiang;Wang, Baiyan;Gu, Shenghao;Wang, Juan;Wang, Guangtao;Guo, Xinyu;Zhao, Chunjiang
关键词:phenotyping; hydration; water absorption; seed emergence; kernel moisture
-
Analysis of stomatal characteristics of maize hybrids and their parental inbred lines during critical reproductive periods
作者:Zhang, Changyu;Jin, Yu;Wang, Jinglu;Zhang, Ying;Lu, Xianju;Guo, Xinyu;Zhang, Changyu;Jin, Yu;Wang, Jinglu;Zhang, Ying;Lu, Xianju;Guo, Xinyu;Zhao, Yanxin;Song, Wei
关键词:maize; hybrids; stomatal phenotypes; high-throughput acquisition; deep learning



