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leaf recognition using BP-RBF hybrid neural network

Journal of Forestry Research 2022年第2期33卷 579-589页

作者： Xin Yang Haiming Ni Jingkui Li Jialuo Lv Hongbo Mu Dawei Qi College of Science Northeast Forestry UniversityHarbin 150040People’s Republic of China

Plant recognition has great potential in forestry research and management.A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and *** process was carried out in three steps:image pretreatment,feature extraction,and leaf *** the image pretreatment processing,an image segmentation method based on hue,saturation and value color space and connected component labeling was presented,which can obtain the complete leaf image without veins and *** BP-RBF hybrid neural network was used to test the influence of shape and texture on species *** recognition accuracy of different classifiers was used to compare classification *** accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%,highest among all the classifiers.

关键词： leaf recognition BP-RBF neural network Image processing Feature extraction Machine learning

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recognition algorithm for plant leaves based on adaptive supervised locally linear embedding

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International Journal of Agricultural and Biological Engineering 2013年第3期6卷 52-57页

作者： Yan Qing Liang Dong Zhang Dongyan Wang Xiu Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China Beijing Research Center for Information Technology in Agriculture Beijing 100097China

Locally linear embedding(LLE)algorithm has a distinct deficiency in practical *** requires users to select the neighborhood parameter,k,which denotes the number of nearest neighbors.A new adaptive method is presented based on supervised LLE in this article.A similarity measure is formed by utilizing the Fisher projection distance,and then it is used as a threshold to select *** samples will produce different k adaptively according to the density of the data *** method is applied to classify plant *** experimental results show that the average classification rate of this new method is up to 92.4%,which is much better than the results from the traditional LLE and supervised LLE.

关键词： supervised locally linear embedding manifold learning Fisher projection adaptive neighbors leaf recognition Precision Agriculture

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