您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Automatic greenhouse pest recognition based on multiple color space features

文献类型： 外文期刊

作者： Yang, Zhankui 1 ; Li, Wenyong 2 ; Li, Ming 2 ; Yang, Xinting 2 ;

作者机构： 1.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China

3.Natl Engn Lab Qual & Safety Traceabil Technol & A, Beijing 100089, Peoples R China

关键词： ensemble learning classifier; greenhouse sticky trap; automated pest recognition and counting; HSI and Lab color spaces; multiple color space features

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：1.731； 五年影响因子：1.659 ）

ISSN： 1934-6344

年卷期： 2021 年 14 卷 2 期

页码：

收录情况： SCI

摘要： Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics. This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment. The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions. If a single color space is used, it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios. Therefore, a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity (HSI) color space and the Canny operator in the B component of the Lab color space was proposed. Then, the segmented results for the two-color spaces were summed and achieved 91.57% segmentation accuracy. Next, because different features of pests contribute differently to the classification of pest species, the study extracted multiple features (e.g., color and shape features) in different color spaces for each segmented pest region to improve the recognition performance. Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73% recognition accuracy. The proposed method is a feasible and effective way to process greenhouse pest images. The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台