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September 10, 2025Journal of Internet Services and Information SecurityOpen Access

Slice Residual U-Net Based Rice Plant Disease Classification Using Convolutional Attentional BiGRU

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Authors

PPPrameetha PaiM S Ramaiah University of Applied Sciences SAS. AmuthaDr. Hari Singh Gour University SPSeema PatilUniversity of Maryland, Baltimore

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Implication

This framework demonstrates improved disease classification accuracy in rice plants using deep learning models, highlighting the importance of timely diagnosis.

Key Points

An accuracy of 99.64% was achieved in classifying diseases affecting rice plants.
Pre-processing techniques included image rescaling and upgraded wiener filtering.
Slice-based residual U-Net was used for image segmentation before classification.
The framework leverages convolutional neural networks for feature extraction and BiGRU for classification tasks.

Cite This Study

Pai et al. (2025) studied this question.

synapsesocial.com/papers/68c1afb954b1d3bfb60e6f68 https://doi.org/10.58346/jisis.2025.i2.028

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Implication

This framework demonstrates improved disease classification accuracy in rice plants using deep learning models, highlighting the importance of timely diagnosis.

Slice Residual U-Net Based Rice Plant Disease Classification Using Convolutional Attentional BiGRU

Discussion

Member takes

Implication

Key Points

Cite This Study

Slice Residual U-Net Based Rice Plant Disease Classification Using Convolutional Attentional BiGRU

Discussion

Member takes

Implication

Key Points

Cite This Study

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