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BURGET, R. CHAUHAN, R. KARNATI, M. DUTTA, M.
Originální název
Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Training;Plant diseases;Computational modeling;Feature extraction;Real-time systems;Telecommunications;Pattern recognition
Autoři
BURGET, R.; CHAUHAN, R.; KARNATI, M.; DUTTA, M.
Vydáno
30. 10. 2023
Místo
Ghent
ISBN
979-8-3503-9328-6
Kniha
2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
Strany od
170
Strany do
175
Strany počet
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/" }