Abstract
The objects of traditional plant identification were too broad and the classification features of it were usually not synthetic and the recognition rate was always slightly low. This paper gives one recognition approach based on supervised locally linear embedding (LLE) and K-nearest neighbors. The recognition results for thirty kinds of broad-leaved trees were realized and the average correct recognition rate reached 98.3%. Comparison with other recognition method demonstrated the proposed method is effective in advancing the recognition rate.
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Feng, Y., Zhang, S. (2009). Supervised Locally Linear Embedding for Plant Leaf Image Feature Extraction. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_1
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DOI: https://doi.org/10.1007/978-3-642-04070-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04069-6
Online ISBN: 978-3-642-04070-2
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