Volume 7, Issue 3
Computation Model on Image Segmentation Threshold of Litchi Cluster Based on Exploratory Analysis

Aixia Guo, Deqin Xiao & Xiangjun Zou

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 441-452.

Published online: 2014-07

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  • Abstract

To construct a litchi harvesting robot, the first key part is the machine vision system which is used to recognize ripe litchi clusters and their main fruit bearing branch. It selects and locates picking points. Hence, in order to establish a threshold computation model used to recognize litchi cluster, the research focus is in recognizing all parts of the litchi image. In this paper, a procedure on how to develop an automatic recognition of litchi cluster, fruits and their main fruit bearing branch guided for litchi harvesting robot is proposed. Firstly, according to the analysis on the specialty of litchi fruits and their main fruit bearing branch, particularity and uncertainty of illumination and environment, an overall scheme on the threshold computation model is used to recognize the litchi cluster based on exploratory analysis and its' application are provided. Secondly, after analyzing and comparing all thresholds, running time and effect on image segmentation by threshold computation methods of the maximum entropy, iterative, Otsu and histogram bimodal method, the interval for recognizing all sorts of litchi clusters is obtained, and a mathematical model for computing threshold to segment litchi cluster is put forward. Finally, all ripe litchi clusters of testing images from 6 groups (all together 120) of differently illuminated (in high light, normal light and backlighting) litchi images from differently-colored main fruit bearing branches (partial red, partial brown and partial brown) collected in natural circumstance are effectively recognized with the threshold segmentation method based on the given computing model, with recognition ratio of 88.89%, 92.0%, 88.24%, and 95.45%, 90.0%, 83.33%, which can satisfy the request of image segmentation on litchi-picking robots in complicated environment.

  • Keywords

Exploratory Analysis Main Fruit Bearing Branch of Litchi Image Threshold Segmentation Vision Location

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@Article{JFBI-7-441, author = {}, title = {Computation Model on Image Segmentation Threshold of Litchi Cluster Based on Exploratory Analysis}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {3}, pages = {441--452}, abstract = {To construct a litchi harvesting robot, the first key part is the machine vision system which is used to recognize ripe litchi clusters and their main fruit bearing branch. It selects and locates picking points. Hence, in order to establish a threshold computation model used to recognize litchi cluster, the research focus is in recognizing all parts of the litchi image. In this paper, a procedure on how to develop an automatic recognition of litchi cluster, fruits and their main fruit bearing branch guided for litchi harvesting robot is proposed. Firstly, according to the analysis on the specialty of litchi fruits and their main fruit bearing branch, particularity and uncertainty of illumination and environment, an overall scheme on the threshold computation model is used to recognize the litchi cluster based on exploratory analysis and its' application are provided. Secondly, after analyzing and comparing all thresholds, running time and effect on image segmentation by threshold computation methods of the maximum entropy, iterative, Otsu and histogram bimodal method, the interval for recognizing all sorts of litchi clusters is obtained, and a mathematical model for computing threshold to segment litchi cluster is put forward. Finally, all ripe litchi clusters of testing images from 6 groups (all together 120) of differently illuminated (in high light, normal light and backlighting) litchi images from differently-colored main fruit bearing branches (partial red, partial brown and partial brown) collected in natural circumstance are effectively recognized with the threshold segmentation method based on the given computing model, with recognition ratio of 88.89%, 92.0%, 88.24%, and 95.45%, 90.0%, 83.33%, which can satisfy the request of image segmentation on litchi-picking robots in complicated environment.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi09201413}, url = {http://global-sci.org/intro/article_detail/jfbi/4799.html} }
TY - JOUR T1 - Computation Model on Image Segmentation Threshold of Litchi Cluster Based on Exploratory Analysis JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 441 EP - 452 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi09201413 UR - https://global-sci.org/intro/article_detail/jfbi/4799.html KW - Exploratory Analysis KW - Main Fruit Bearing Branch of Litchi KW - Image Threshold Segmentation KW - Vision Location AB - To construct a litchi harvesting robot, the first key part is the machine vision system which is used to recognize ripe litchi clusters and their main fruit bearing branch. It selects and locates picking points. Hence, in order to establish a threshold computation model used to recognize litchi cluster, the research focus is in recognizing all parts of the litchi image. In this paper, a procedure on how to develop an automatic recognition of litchi cluster, fruits and their main fruit bearing branch guided for litchi harvesting robot is proposed. Firstly, according to the analysis on the specialty of litchi fruits and their main fruit bearing branch, particularity and uncertainty of illumination and environment, an overall scheme on the threshold computation model is used to recognize the litchi cluster based on exploratory analysis and its' application are provided. Secondly, after analyzing and comparing all thresholds, running time and effect on image segmentation by threshold computation methods of the maximum entropy, iterative, Otsu and histogram bimodal method, the interval for recognizing all sorts of litchi clusters is obtained, and a mathematical model for computing threshold to segment litchi cluster is put forward. Finally, all ripe litchi clusters of testing images from 6 groups (all together 120) of differently illuminated (in high light, normal light and backlighting) litchi images from differently-colored main fruit bearing branches (partial red, partial brown and partial brown) collected in natural circumstance are effectively recognized with the threshold segmentation method based on the given computing model, with recognition ratio of 88.89%, 92.0%, 88.24%, and 95.45%, 90.0%, 83.33%, which can satisfy the request of image segmentation on litchi-picking robots in complicated environment.
Aixia Guo, Deqin Xiao & Xiangjun Zou. (2019). Computation Model on Image Segmentation Threshold of Litchi Cluster Based on Exploratory Analysis. Journal of Fiber Bioengineering and Informatics. 7 (3). 441-452. doi:10.3993/jfbi09201413
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