Journal of Intelligent Systems (Apr 2023)

A multi-crop disease identification approach based on residual attention learning

  • Kirti,
  • Rajpal Navin

DOI
https://doi.org/10.1515/jisys-2022-0248
Journal volume & issue
Vol. 32, no. 1
pp. 113 – 32

Abstract

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In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types by analyzing leaf images of the plants. A total of four datasets are used for the work. The system incorporates attention-aware features computed by the Residual Attention Network (Res-ATTEN). The base of the network is ResNet-18 architecture. Integrating attention learning in the residual network helps improve the system's overall accuracy. Various residual attention units are combined to create a single architecture. Unlike the traditional attention network architectures, which focus only on a single type of attention, the system uses a mixed type of attention learning, i.e., a combination of spatial and channel attention. Our technique achieves state-of-the-art performance with the highest accuracy of 99%. The results show that the proposed system has performed well for both purposes and notably outperformed the traditional systems.

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