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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU150168" target="_blank" >RIV/00216305:26220/23:PU150168 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10333302/" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10333302/</a>
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/VK01010153" target="_blank" >VK01010153: Development of artificial intelligence for multimodal non-destructive forensic material analysis system</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3503-9328-6
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
170-175
Publisher name
Neuveden
Place of publication
Ghent
Event location
Gent, Belgium
Event date
Oct 30, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)