自然光照下智能叶菜收获机作业参数的获取

doi:10.3969/j.issn.1004-1524.2017.11.21

浙江农业学报 ›› 2017, Vol. 29 ›› Issue (11): 1930-1937.DOI: 10.3969/j.issn.1004-1524.2017.11.21

自然光照下智能叶菜收获机作业参数的获取

伍渊远, 尚欣^*, 张呈彬, 谢新义

宁夏大学机械工程学院,宁夏银川 750021

收稿日期:2017-05-02 出版日期:2017-11-20 发布日期:2017-12-05
通讯作者: 尚欣,E-mail:XKshangx@163.com
作者简介:伍渊远(1992—),男,四川达州人,硕士研究生,主要从事智能农业装备研究。E-mail:keatswu@163.com
基金资助:
宁夏回族自治区西部一流学科建设项目(机械工程); 宁夏大学研究生创新项目(GIP2017019)

Acquisition of operation parameters of intelligent leaf vegetable harvester under natural lighting

WU Yuanyuan, SHANG Xin^*, ZHANG Chengbin, XIE Xinyi

School of Mechanical Engineering, Ningxia University, Yinchuan 750021, China

Received:2017-05-02 Online:2017-11-20 Published:2017-12-05

摘要/Abstract

摘要： 针对智能叶菜收获机作业过程中需自主获取导航参数与割台高度调整参数的问题,提出了利用机器视觉技术获取这两种作业参数的方法。首先对自主获取导航参数进行研究,将采集的叶菜田图像进行预处理、获取导航离散点,利用稳健回归法对离散点进行线性拟合进而获得导航控制参数,以便收获机调整作业方向;然后对于割台高度调节参数,将叶菜割茬图像预处理及割茬高度特征提取以获得割茬高度,利用割茬高度作为收获机割台高度调整的参数。结果表明,导航线准确识别率为97%,留茬高度的平均测量误差为8 mm,最大相对误差为11.9%。说明该方法在自然光照下,能有效获取作业方向参数和留茬高度,为无人驾驶式收获机的智能、精准作业提供了技术支持。

关键词: 机器视觉, 智能收获机, 导航参数, 割茬高度

Abstract: Aiming at the problem of obtaining navigation parameters and the height of cutting platform parameters autonomously during the operation of intelligent leaf vegetable harvester, the method of obtaining two kinds of job parameters by machine vision was proposed. Firstly, the autonomous navigation parameters were studied, the collected images of leaf vegetables were pretreated and the navigation discrete points were obtained. The robust regression method was used to linearly fit the discrete points to obtain the navigation control parameters so that the harvester could adjust the direction of the work. Then the parameters for the height of the cutting table, image preprocessing and stubble height feature extraction of leafy vegetables were adjusted. The stubble height was used as the parameter of the height of the cutting table of the harvester. The results showed that the accurate line recognition rate was 97%, and the relative error of stubble height was 8 mm, the maximum relative error was 11.9%. It showed that the method could effectively extract the direction parameters and stubble height under natural light, which provided technical support for intelligent and accurate operation of unmanned harvester.

Key words: machine vision, intelligent harvester, navigation parameter, stubble height

中图分类号:

S126

伍渊远, 尚欣, 张呈彬, 谢新义. 自然光照下智能叶菜收获机作业参数的获取[J]. 浙江农业学报, 2017, 29(11): 1930-1937.

WU Yuanyuan, SHANG Xin, ZHANG Chengbin, XIE Xinyi. Acquisition of operation parameters of intelligent leaf vegetable harvester under natural lighting[J]. , 2017, 29(11): 1930-1937.

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自然光照下智能叶菜收获机作业参数的获取

Acquisition of operation parameters of intelligent leaf vegetable harvester under natural lighting

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