Deep-Plant: Plant Identification with convolutional neural networks

This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visu...

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Veröffentlicht in:	arXiv.org 2015-06
Hauptverfasser:	Sue Han Lee, Chan, Chee Seng, Wilkin, Paul, Remagnino, Paolo
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Sprache:	eng
Schlagworte:	Artificial neural networks Black boxes Flowers & plants Neural networks
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creator	Sue Han Lee Chan, Chee Seng Wilkin, Paul Remagnino, Paolo
description	This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
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subjects	Artificial neural networks Black boxes Flowers & plants Neural networks
title	Deep-Plant: Plant Identification with convolutional neural networks
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