Soft Computing

, Volume 22, Issue 22, pp 7649–7658 | Cite as

Research on weeds identification based on K-means feature learning

  • JingLei TangEmail author
  • ZhiGuang Zhang
  • Dong Wang
  • Jing Xin
  • LiJun He
Methodologies and Application


This paper aims to overcome the unstable identification results and weak generalization ability in feature extraction based on manual design to realize the automatic weeds identification. On the basis of unsupervised feature learning identification model, K-means clustering algorithm after data preprocessing is used to realize feature learning and construct feature dictionary. Then this feature dictionary is used to extract features from labeled data and train the classification model to realize the automatic weeds identification. In this process, this paper focuses on the effect of parameters such as the clustering number to identification accuracy under single-layer network structure, and the identification accuracy between the single-layer and the two-layer network structure was compared and analyzed. Experimental results show that identification rate can be improved by increasing the network levels, as well as fine-tuning the parameters under the premise of selecting reasonable parameters.


Feature learning K-means clustering algorithm Feature dictionary Weeds identification 



This study was funded by Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing (Grant No. 2016CP01), Xi’an Science and Technology Plan Projects (Grant No. NC1504(2)), the National Natural Science Foundation of China (Grant Nos. 31101075, 61402375), Natural Science Fundamental Research Plan of Shaanxi Province (Grant No. 2016JM6038), Fundamental Research Funds for the Central Universities, NWSUAF (Grant No. 2452015060).

Compliance with ethical standards

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • JingLei Tang
    • 1
    • 2
    Email author
  • ZhiGuang Zhang
    • 3
  • Dong Wang
    • 1
  • Jing Xin
    • 2
  • LiJun He
    • 1
  1. 1.College of Information EngineeringNorthwest A&F UniversityYanglingPeople’s Republic of China
  2. 2.Shaanxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi’an University of TechnologyXi’anPeople’s Republic of China
  3. 3.School of Mathematics and Information Science and TechnologyHebei Normal University of Science and TechnologyQinhuangdaoPeople’s Republic of China

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