2015
DOI: 10.1016/j.patrec.2015.02.010
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Plant leaf recognition using texture and shape features with neural classifiers

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Cited by 212 publications
(96 citation statements)
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“…The comparison of the present results with the results of [9] are not directly comparable due to the differences in the datasets used. Despite of this fact this work compares its results with the results of [9].…”
Section: A Analysis On the Basis Of Texture Feature Data Setscontrasting
confidence: 72%
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“…The comparison of the present results with the results of [9] are not directly comparable due to the differences in the datasets used. Despite of this fact this work compares its results with the results of [9].…”
Section: A Analysis On the Basis Of Texture Feature Data Setscontrasting
confidence: 72%
“…The comparison of the present results with the results of [9] are not directly comparable due to the differences in the datasets used. Despite of this fact this work compares its results with the results of [9]. In [9] two classification algorithms Neuro Fuzzy Controller(NFC) and Multi-Layer Perceptron(MLP) have been used for the texture based model with only dorsal side images and the average predictive accuracy achieved is 81.6% and 87% respectively.…”
Section: A Analysis On the Basis Of Texture Feature Data Setscontrasting
confidence: 72%
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“…Sixteen inputs (6 geometrical, 8 texture and 2 morphological features) were fed to an artificial neural network (ANN) with 60 nodes in the hidden layer and a learning rate of 0.1 over 50000 generations. Using the same dataset, Chaki et al (2015) achieved an overall accuracy of 97.6% using a Neuro-Fuzzy classifier (NFC) with a 44-element texture vector and a 3-element shape vector [15]. Using shape features only on the Flavia dataset and Pattern Net (a flavour of neural network), Siravenha and Carvalho [16] reached a similar accuracy as Chaki et al [15].…”
Section: IImentioning
confidence: 72%
“…Using the same dataset, Chaki et al (2015) achieved an overall accuracy of 97.6% using a Neuro-Fuzzy classifier (NFC) with a 44-element texture vector and a 3-element shape vector [15]. Using shape features only on the Flavia dataset and Pattern Net (a flavour of neural network), Siravenha and Carvalho [16] reached a similar accuracy as Chaki et al [15]. Their feed-forward neural network had two hidden layers with 26 neurons in each and it was trained over 100 epochs.…”
Section: IImentioning
confidence: 99%