利用融合高度与单目图像特征的支持向量机模型识别杂草

被引：33

作者：

王璨

李志伟

机构：

[1] 山西农业大学工学院

来源：

农业工程学报 | 2016年 / 32卷 / 15期

关键词：

双目视觉; 支持向量机; 特征提取; 杂草识别; 双目图像; 特征融合;

D O I：

暂无

中图分类号：

TP391.41 [];

学科分类号：

080203 ;

摘要：

除草是保证农作物高产的必要工作。针对机械化除草和智能喷药中存在的杂草识别问题,以2～5叶苗期玉米及杂草为研究对象,进行了融合高度特征与单目图像特征的杂草识别方法研究。首先从单目图像中提取16个形态特征和2个纹理特征;然后基于双目图像,提出了针对植株的高度特征提取方法,所得高度特征与实际测量值间误差在±12 mm以内;利用max-min ant system算法对形态特征进行优化选择,将形态特征减少到6个,有效减少数据量62.5%,并与纹理和高度特征进行融合;将2～5叶玉米幼苗的可除草期划分为3个阶段,分别构建融合高度特征与单目图像特征的SVM识别模型,并与相应不含高度特征模型进行对比。经测试,3个阶段模型的识别准确率分别为96.67%,100%,98.33%;平均识别准确率达98.33%。不含高度特征模型的识别准确率分别为93.33%,91.67%,95%;平均识别准确率为93.33%。结果表明,融合高度特征与单目图像特征的SVM识别模型优于不含高度特征模型,平均识别准确率提高了5百分点。该方法实现了高准确率的杂草识别,研究结果为农业精确除草的发展提供参考。

引用

页码：165 / 174

页数：10

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