An end-to-end deep learning system for hop classification.
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Date
2022
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Abstract
Automatic classification of plant species is a very
challenging and widely studied problem in the literature. Distinguishing different varieties within the same species is an even
more challenging task although less explored. Nevertheless, for
some species distinguishing the varieties within the species can
be of paramount importance.
Hops, a plant widely used in beer production, has over
250 cataloged varieties. Although the varieties have similar
appearances, their chemical components, which influence the
aroma and flavor of the drink, are quite heterogeneous. Therefore,
it is important for producers to distinguish which variety the
plant belongs to in a simple manner.
In this work, an end-to-end deep learning system is proposed
to automate the task of hop classification. Five architectures are
proposed and evaluated with an uncontrolled environment dataset
that includes 12 varieties of hops on 1592 images, from three
different cell phone cameras. The best architecture automatically
detects the hop leaves on the image and performs the classification
using the information of up to 10 leaves. The method achieved an
accuracy of 95.69% with an inference time of 672ms. To reach
such figures, state-of-the-art convolutional blocks were explored
along with data augmentation techniques. Our results show that
the system is robust and has a low computational cost.
Description
Keywords
Convolutional neural network, Leaf recognition, Data augmentation
Citation
CASTRO, P. H. N.; MOREIRA, G. J. P.; LUZ, E. J. da S. An end-to-end deep learning system for hop classification. IEEE Latin America Transactions, v. 20, n. 3, p. 430-442, mar. 2022. Disponível em: <https://latamt.ieeer9.org/index.php/transactions/article/view/5785>. Acesso em: 06 jul. 2023.