Research on weeds identification based on K-means feature learning
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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.
KeywordsFeature 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.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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