Publication detail

Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network

BURGET, R. CHAUHAN, R. KARNATI, M. DUTTA, M.

Original Title

Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network

Type

conference paper

Language

English

Original Abstract

The early detection of plant diseases reduces agricultural loss. In the field of computer vision and pattern recognition, deep learning (DL) techniques, particularly convolutional neural networks (CNNs), are widely employed. To identify plant diseases, researchers put forth various DL models. However, DL models require many parameters to learn the underlying patterns of the plant disease, increasing training time and making it challenging to deploy on small devices. This study introduces a novel DL model utilizing a dual self-attention modified residual-inception network (DARINet), which integrates the multi-scale, self-attention, and channel attention features with the residual connection. The proposed approach is evaluated on two plant disease datasets such as Cassava and Rice leaf, achieving an accuracy of 77.12% and 98.92%. In Comparision to state-of-the-art DL models, our proposed approach attains higher accuracy with fewer parameters.

Keywords

Training;Plant diseases;Computational modeling;Feature extraction;Real-time systems;Telecommunications;Pattern recognition

Authors

BURGET, R.; CHAUHAN, R.; KARNATI, M.; DUTTA, M.

Released

30. 10. 2023

Location

Ghent

ISBN

979-8-3503-9328-6

Book

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

Pages from

170

Pages to

175

Pages count

6

URL

https://ieeexplore.ieee.org/abstract/document/10333302/

BibTex

@inproceedings{BUT187111,
  author="Radim {Burget} and Rashi {Chauhan} and Mohan {Karnati} and Malay Kishore {Dutta}",
  title="Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network",
  booktitle="2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)",
  year="2023",
  pages="170--175",
  address="Ghent",
  isbn="979-8-3503-9328-6",
  url="https://ieeexplore.ieee.org/abstract/document/10333302/"
}