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Disentangled Representation Learning for Leaf Diseases Recognition

  • Xing Wang
  • Congcong Zhu
  • Suping WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11901)

Abstract

Plant disease detection plays an important role in agricultural production and ecological protection. However, it is always a challenge to detect the severity of plant diseases in multi-species and multi-disease conditions. Unlike most existing classification methods which are difficult to solve multi-properties detection, we propose a disentangled representation interactive network (DRIN), which disentangles the global features of each plant leaf and learns the discriminative representation of multiple sub-properties, including plant species, disease types and disease severity. To achieve it, the disentangled representation network transform the joint probability into the conditional probability through the information interaction between the sub-properties. Moreover, data filtering was introduced to reduce the error messages in property interactions. Experimental results demonstrate the effectiveness of our DRIN on the plant disease detection dataset.

Keywords

Representation learning Plant disease detection Information interaction Data filtering 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Information EngineeringNingxia UniversityYinchuanChina

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