基于形态学图像检测的机械手移栽穴苗识别技术

王跃勇; 于海业; 刘媛媛

doi:10.3788/OPE.20162403.0651

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基于形态学图像检测的机械手移栽穴苗识别技术

信息科学 | 更新时间：2020-08-13

- 基于形态学图像检测的机械手移栽穴苗识别技术
- Recognition of seedlings in mechanical transplanting processing by Morphological image detection
- 光学精密工程 2016年24卷第3期页码：651-658
- 作者机构：
  
  1. 吉林大学工程仿生教育部重点实验室,吉林长春,130022
  2. 吉林大学通信工程学院, 吉林长春 130012
  3. 吉林大学生物与农业工程学院,吉林长春,130022
  4. 吉林农业大学信息技术学院,吉林长春,130118
  5. 长春市农业机械研究院, 吉林长春 130052
- 作者简介：
- 基金信息：
  
  国家836高技术研究发展计划资助项目(No.2012AA10A506)();国家自然科学基金资助项目(No.61171078, No.61271315)()
- DOI：10.3788/OPE.20162403.0651
  中图分类号： S24;TP751
- 收稿日期：2015-10-29，
  
  修回日期：2015-12-07，
  
  纸质出版日期：2016-03-25
- 稿件说明：
移动端阅览
王跃勇, 于海业, 刘媛媛. 基于形态学图像检测的机械手移栽穴苗识别技术[J]. 光学精密工程, 2016,24(3): 651-658

WANG Yue-yong, YU Hai-ye, LIU Yuan-yuan. Recognition of seedlings in mechanical transplanting processing by Morphological image detection[J]. Editorial Office of Optics and Precision Engineering, 2016,24(3): 651-658
王跃勇, 于海业, 刘媛媛. 基于形态学图像检测的机械手移栽穴苗识别技术[J]. 光学精密工程, 2016,24(3): 651-658 DOI： 10.3788/OPE.20162403.0651.

WANG Yue-yong, YU Hai-ye, LIU Yuan-yuan. Recognition of seedlings in mechanical transplanting processing by Morphological image detection[J]. Editorial Office of Optics and Precision Engineering, 2016,24(3): 651-658 DOI： 10.3788/OPE.20162403.0651.

摘要

针对机械移栽穴苗过程中如何有效识别出根系受损的幼苗

进而及时进行分类、补救这一实际问题

提出一种基于机器视觉的移栽穴苗识别方法。该方法根据机械手移栽穴苗特点

对比检测图像土壤基质面积与基准面积;进而通过测定土壤基质完整率

判断样本合格与否。文中从理论和实际的角度说明了形态学图像处理方法处理穴苗移栽图像特点

设计了机械手移栽穴苗识别在线测试系统。最后

对基于形态学图像检测方法的机械手移栽穴苗识别技术和普通图像检测方法进行对比实验。结果表明

本文提出的形态学检测方法对一个72孔穴盘合格检出率提高了6.6%

不合格检出率提高了54.5%。同时

一个穴苗的平均处理时间约为1.82 s。结果表明提出的方法可靠

耗时较短

能够匹配机械手移栽流水线作业时间上的要求

并满足实时处理要求。

Abstract

How to discover and remedy the damaged seedlings quickly in batch processing seedlings by automatic transplanting technology was researched. A recognition method of seedlings in mechanical transplanting processing based on machine vision was proposed. Based on the characteristics of mechanical transplanting seedlings

the method was used to measure soil matrix intact rate of seedlings by comparison of the image areas between soil matrix and benchmark. By which the samples were qualified or not could be judged. Then

the image characteristics processed by morphological image processing method in mechanical transplanting seedlings were described from the theory and practice and an online testing system for recognition of mechanical transplanting seedlings was designed. Finally

the manipulator transplanting seedling method based on morphological image detection and the common image detection method were compared. Experimental results indicate that the morphological detection method proposed in this paper for a seedling tray containing 72 holes increases the detection rate by 6.6%

and unqualified detection rate by 54.5%. At the same time

the average time that processes one seeding is about 1.82 s. It is shown that the proposed method is reliable

short time-consuming

satisfied with the requirement of real-time processing.

关键词

Keywords

references

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