@article {10.3844/jcssp.2026.2879.2890,
article_type = {journal},
title = {Dual-Branch Shape Texture Learning With Pyramid Residual and Sobel Edge Layers for Rice Variety Classification},
author = {Pradeep, M and Siddappa, M},
volume = {22},
number = {9},
year = {2026},
month = {Sep},
pages = {2879-2890},
doi = {10.3844/jcssp.2026.2879.2890},
url = {https://thescipub.com/abstract/jcssp.2026.2879.2890},
abstract = {Automated rice variety classification categorizes rice types based on features, such as grain texture, shape, color, and size. Traditional handcrafted feature-based models fail at rice classification due to lighting variations, background noise, and environmental conditions, leading to overlapping visual characteristics and frequent misclassification. To address these challenges, this research proposes a novel Dual-Branch Convolutional Neural Network with Pyramid Residual Units and a Sobel Edge Layer (DB-PRU-SEL) for rice variety classification. The DB-PRU-SEL consists of two branches: The first extracts robust shape features using hierarchical PRU to capture multi-scale structural details, while the second focuses on texture by integrating Sobel edge detection with convolutional layers. The outputs from both branches are concatenated by an attention-guided feature fusion mechanism that selectively enhances the discriminative features before passing them through fully connected layers for final classification. DB-PRU-SEL achieves superior performance on a rice image dataset, attaining an accuracy of 99.87% and an Area Under the Curve (AUC) of 99.99%, respectively. Comparative and ablation studies demonstrate that DB-PRU-SEL outperforms conventional classifiers. Additionally, complexity analysis indicates reduced training time and memory usage, making it an efficient solution for rice variety classification.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}