Deep-Plant: Plant Identification with convolutional neural networks
This work addresses plant identification for botanists or ecologists, but it is incremental as it applies existing CNN methods to a new dataset.
The paper tackled plant species identification by using convolutional neural networks (CNNs) to learn unsupervised feature representations for 44 species, achieving superior results compared to state-of-the-art hand-crafted feature methods.
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.