An End-to-End Deep Learning System for Hop Classification



Hop, Convolutional neural network, Leaf recognition, Data augmentation


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.


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Author Biographies

PEDRO HENRIQUE NASCIMENTO CASTRO, Universidade Federal de Ouro Preto

Pedro Castro é bacharel e mestre em Ciência da Computação pela Universidade Federal de Ouro Preto (UFOP), em 2008 e 2012, respectivamente. Atualmente, é Técnico Administrativo no Núcleo de Tecnologia da Informação (NTI) da UFOP. Seus interesses de pesquisa incluem meta-heurística, sistemas embarcados, aprendizagem de máquinas e aprendizagem profunda.

Gladston Juliano Prates Moreira, Universidade Federal de Ouro Preto

Gladston Moreira é mestre em matemática pela Universidade Federal de Minas Gerais em 2003, e doutor em engenharia elétrica em 2011. Atualmente é Professor Associado do Departamento de Computação da Universidade Federal de Ouro Preto, e professor permanente do Programa de Pós-Graduação em Ciência da Computação. Seus interesses de pesquisa incluem otimização multiobjetivo, reconhecimento de padrões e estatística espacial.

Eduardo José da Silva Luz, Universidade Federal de Ouro Preto

Eduardo Luz é bacharel em Engenharia Elétrica pela Universidade Federal de Minas Gerais (2005), e doutor em Ciência da Computação pela UniversidadeFederal de Ouro Preto (2019). Atua como Professor Adjunto no Departamento de Computação (DECOM) da Universidade Federal de Ouro Preto e nos cursos de pós-graduação em Ciência da Computação e Mestrado Profissional em Instrumentação, Controle e Automação. Seus interesses de pesquisa incluem processamento de sinais biomédicos, sistemas embarcados, reconhecimento de padrões e aprendizagem de máquina.


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How to Cite

CASTRO, . P. H. N., Moreira, G. J. P. ., & Luz, E. J. da S. (2021). An End-to-End Deep Learning System for Hop Classification. IEEE Latin America Transactions, 20(3), 430–442. Retrieved from