• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality >Crop Species Identification using Machine Vision of Computer Extracted Individual Leaves
【24h】

Crop Species Identification using Machine Vision of Computer Extracted Individual Leaves

机译：使用计算机提取的单个叶片的机器视觉识别作物物种

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An unsupervised method for plant species identification was developed which uses computer extracted individual whole leaves from color images of crop canopies. Green canopies were isolated from soil/residue backgrounds using a modified Excess Green and Excess Red separation method. Connected components of isolated green regions of interest were changed into pixel fragments using the Gustafson-Kessel fuzzy clustering method. The fragments were reassembled as individual leaves using a genetic optimization algorithm and a fitness method. Pixels of whole leaves were then analyzed using the elliptic Fourier shape and Haralick's classical textural feature analyses. A binary template was constructed to represent each selected leaf region of interest. Elliptic Fourier descriptors were generated from a chain encoding of the leaf boundary. Leaf template orientation was corrected by rotating each extracted leaf to a standard horizontal position. This was done using information provided from the first harmonic set of coefficients. Textural features were computed from the grayscale co-occurrence matrix of the leaf pixel set. Standardized leaf orientation significantly improved the leaf textural venation results. Principle component analysis from SAS® was used to select the best Fourier descriptors and textural indices. Indices of local homogeneity, and entropy were found to contribute to improved classification rates. A SAS classification model was developed and correctly classified 83% of redroot pigweed, 100% of sunflower 83% of soybean, and 73% of velvetleaf species. An overall plant species correct classification rate of 86% was attained.
机译：开发了一种无监督的植物物种识别方法，该方法使用计算机从农作物冠层的彩色图像中提取出的整片叶子进行提取。使用改良的“过量绿色”和“过量红色”分离方法从土壤/残留物背景中分离出绿色的树冠。使用Gustafson-Kessel模糊聚类方法将孤立的感兴趣的绿色区域的连接组件更改为像素片段。使用遗传优化算法和适应度方法，将片段重新组装为单个叶子。然后使用椭圆傅立叶形状和Haralick的经典纹理特征分析来分析整片叶子的像素。构建二元模板以表示每个选定的目标叶区域。椭圆傅立叶描述符是从叶边界的链编码生成的。通过将每个提取的叶子旋转到标准水平位置来校正叶子模板的方向。这是使用从第一谐波系数组提供的信息完成的。从叶像素集的灰度共现矩阵计算纹理特征。标准化的叶子方向显着改善了叶子纹理的通透效果。使用SAS®的主成分分析来选择最佳的Fourier描述符和纹理索引。发现局部同质性和熵的指数有助于提高分类率。建立了SAS分类模型，正确分类了83％的红根杂草，100％的向日葵，83％的大豆和73％的丝绒种。总体植物正确分类率达到86％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号