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State-of-the-Art Deep Learning Architectures for Identifyin..:
, In:
2024 4th International Conference on Data Engineering and Communication Systems (ICDECS)
,
Asha Rani, K P
;
Gowrishankar, S
- p. 1-6 , 2024
Link:
https://doi.org/10.1109/ICDECS59733.2023.10503021
RT T1 <cTitle>2024 4th International Conference on Data Engineering and Communication Systems (ICDECS)</cTitle>: T1 <Title>State-of-the-Art Deep Learning Architectures for Identifying Diseases in Cucumber and Black Gram Plants</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10503021&Exemplar=1&LAN=DE A1 Asha Rani, K P A1 Gowrishankar, S YR 2024 K1 Adaptation models K1 Plant diseases K1 Extreme learning machines K1 Crops K1 Feature extraction K1 Robustness K1 Convolutional neural networks K1 plant disease identification K1 deep learning architectures K1 Extreme Learning Machine (ELM) K1 Feedforward Neural Network (FNN) K1 Deep Residual Network K1 Convolutional Neural Network (CNN) K1 MobileNet K1 Cucumber and Black Gram Disease Classification K1 agricultural technology SP 1 OP 6 LK http://dx.doi.org/https://doi.org/10.1109/ICDECS59733.2023.10503021 DO https://doi.org/10.1109/ICDECS59733.2023.10503021 SF ELIB - SuUB Bremen
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