Calculation of light distribution of apple tree canopy based on color characteristics of 3D point cloud

被引:0
|
作者
Ma, Xiaodan [1 ,2 ]
Guo, Cailing [1 ]
Zhang, Xue [1 ]
Ma, Li [1 ]
Zhang, Lijiao [1 ]
Liu, Gang [1 ]
机构
[1] Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing,100083, China
[2] College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing,163319, China
关键词
Mathematical morphology - Fruits - Light - Color - Forestry - Fuzzy inference;
D O I
10.6041/j.issn.1000-1298.2015.06.038
中图分类号
学科分类号
摘要
Study on light distribution of apple tree canopy is one of the important ways to optimize type of fruit trees and improve potential production. The reasonable and effective use of light energy and optimization of light distribution within fruit tree canopy have vital significance for the formation of fruit tree growth and fruit quality. Calculation method of light distribution of free spindle apple canopy was carried out. In recent years, there were several researches on light distribution of plant canopy on the basis of three dimensional morphology of plant canopy by using mathematical simulation methods which could not express the real light distribution. In order to reveal real light distribution rule of canopy intelligently and efficiently, apple trees of spindle shape were selected as research objects, and based on the correlation that the target image color changed with the light intensity. Firstly, 3D point cloud of apple tree canopy in leaf curtain stability period was captured by Trimble TX5 laser scanner; secondly, according to the actual canopy division method, color information of different areas in 3D canopy space was extracted; thirdly, aiming at the shortcomings of complexity, fuzziness and indescribability by precise and quantitative symbols under natural environment, the fuzzy neural network was constructed to predict light distribution with color characteristic as input and relative light intensity as output. The experimental results showed that the precision of the proposed method had good feasibility, the prediction accuracy was 80.57%. The result of this study will provide theoretical basis for scientific pruning to get the best light distribution. ©, 2015, Chinese Society of Agricultural Machinery. All right reserved.
引用
收藏
页码:263 / 268
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