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Morphology-Aware Lightweight Deep Learning for Explainable Rice Variety Classification

Precious Mojolaoluwa Ojo, Benjamin Chiemeka Opara, Confidence Chigozirim, Olumba, Miracle Ugomma Anunobi, Raymond James Sunday, Grace Ugochi Nneji, Richard Iherorochi, Nneji, Edwin Sunday Umana

Abstract

This research shows a lightweight convolutional neural network framework for automatic rice variety classification. The proposed model is multi-branch modules of Inception style, res connection, channel – spatial attention mechanism to improve feature extraction without increasing the architecture compact and efficient. A rice image dataset of 5 varieties is used for training and testing the network, and the network model structure and Hyperparameters are improved by a sequence of tests. Results show that the approach we have proposed is able to achieve very high classification performance, indicating good prospects of practical application to automated rice quality control. To improve the trustworthiness more, explainable AI techniques, namely Grad-CAM, LIME and SHAP are being utilized, and the visualizations show that the model's predictions are primarily influenced by grain shape and texture features.

Keywords

rice classification; convolutional neural network; attention mechanism; explainable AI; Grad-CAM; LIME; SHAP

References

Ahnaf Alavee, K., Hasan, M., Hasnayen Zillanee, A., Mostakim, M., Uddin, J., Silva Alvarado, E., de la Torre Diez, I., Ashraf, I., & Abdus Samad, M. (2024). Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence. IEEE Access, 12, 73950–73969. https://doi.org/10.1109/ACCESS.2024.3405570 Fu, J., Liu, J., Jiang, J., Li, Y., Bao, Y., & Lu, H. (2021). Scene Segmentation With Dual Relation- Aware Attention Network. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2547–2560. https://doi.org/10.1109/TNNLS.2020.3006524 G. Li, I. Yun, J. Kim, and J. K. (2019). DABNet: Depth-wise Asymmetric Bottleneck for Real- time Semantic Segmentation. ArXiv: ArXiv:1907.11357. https://doi.org/10.48550/arXiv.1907.11357 Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022). Attention mechanisms in computer vision: A survey. Computational Visual Media, 8(3), 331–368. https://doi.org/10.1007/s41095-022-0271-y Islam, Md. M., Himel, G. M. S., Moazzam, Md. G., & Uddin, M. S. (2025). Artificial Intelligence- based Rice Variety Classification: A State-of-the-art Review and Future Directions. Smart Agricultural Technology, 10, 100788. https://doi.org/10.1016/j.atech.2025.100788 Koklu, M., Cinar, I., & Taspinar, Y. S. (2021). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. https://doi.org/10.1016/j.compag.2021.106285 M. A. I. Aminudin, M. N. Abdullah, F. Mustapha, K. K. Eng, M. Mustapha, and A. M. (2025). Explainable Deep Learning Framework for Binary Corrosion Image Classification Using Grad-CAM. Sensors, 25(22), 7070. Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Nahar, S., Jackson, J., & Chikwendu, I. A. (2022). WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis. Diagnostics, 12(3), 765. https://doi.org/10.3390/diagnostics12030765 Monday, H. N., Li, J., Nneji, G. U., Nahar, S., Hossin, M. A., Jackson, J., & Ejiyi, C. J. (2022). COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics, 12(3), 741. https://doi.org/10.3390/diagnostics12030741 Nneji, G. U., Cai, J., Monday, H. N., Hossin, M. A., Nahar, S., Mgbejime, G. T., & Deng, J. (2022). Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification. Diagnostics, 12(3), 717. https://doi.org/10.3390/diagnostics12030717 S.-H. Lee, L.-C. Yan, and C.-S. Y. (2023). LIRNet: A lightweight inception residual convolutional network for solar panel defect classification. Energies, 16(5), 2112.

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