International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 1627KB)
RiceVision: A Cross-Platform System for Real-Time Rice Variety Identification Using Deep Learning
PDF (1627KB), PP.105-119
Views: 0 Downloads: 0
Author(s)
Al Hossain Abid
1
Mirza Niaz Morshed
1
Md. Ashif-Ul-Haque
1
Md Masudul Islam
1,*
Md. Shafiqul Islam
2
Index Terms
Rice Variety Identification, Software Architecture, Web Application, Smart Agriculture
Abstract
This study presents RiceVision, a cross-platform software system for real-time rice variety identification using deep learning–based image analysis. Unlike prior work that primarily focuses on classification accuracy, RiceVision emphasizes reproducibility, deployment, and usability in real-world agricultural environments. The system integrates a web-based platform and an offline-capable Android application within a unified architecture, ensuring consistent preprocessing and inference across platforms. Deep learning models are deployed using TensorFlow and TensorFlow Lite to support both online and on-device inference. The proposed hybrid framework combines convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures using a stacked ensemble strategy. Experimental evaluation on a 62-class rice variety dataset demonstrated strong classification performance, where the stacked ensemble achieved an average 5-fold validation accuracy of 98.64%, outperforming individual VGG16 (90.64%) and ViT-B/16 (91.28%) models. The system further demonstrated stable convergence behavior and low inter-fold variance, indicating robust generalization capability. A centralized model management mechanism enables version control and seamless updates across deployment platforms. Detailed model configurations, validation results, and explainability analyses are provided in the Supplementary Material. RiceVision highlights the potential of deployable AI systems for practical decision support in digital agriculture.
Cite This Paper
Al Hossain Abid, Mirza Niaz Morshed, Md. Ashif-Ul-Haque, Md. Masudul Islam, Md. Shafiqul Islam, "RiceVision: A Cross-Platform System for Real-Time Rice Variety Identification Using Deep Learning", International Journal of Education and Management Engineering (IJEME), Vol.16, No.3, pp. 105-119, 2026. DOI:10.5815/ijeme.2026.03.08
Reference
[1]Md. M. Islam, G. M. S. Himel, Md. G. Moazzam, and M. S. Uddin. (2025a), “Artificial Intelligence-based Rice Variety Classification: A State-of-the-Art Review and Future Directions,” Smart Agricultural Technology, vol. 10, p. 100788, Jan. 2025, doi: https://doi.org/10.1016/j.atech.2025.100788.
[2]Islam, M. M., Himel, G. M. S., Uddin, M. S., & Moazzam, M. G. (2024). A visual dataset for recognition of rice varieties. In Data in Brief (Vol. 54, p. 110442). Elsevier BV. https://doi.org/10.1016/j.dib.2024.110442
[3]Cinar and M. Koklu, “Classification of Rice Varieties Using Artificial Intelligence Methods,” International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 3. International Journal of Intelligent Systems and Applications in Engineering, pp. 188–194, Sep. 30, 2019. doi: 10.18201/ijisae.2019355381
[4]Tahsin, M., Matin, M. M. H., Khandaker, M., Reemu, R. S., Arnab, M. I., Rashid, M. R. A., Rasel, M. M. K., Islam, M. M., Islam, M., & Ali, M. S. (2024). An extensive image dataset for deep learning-based classification of rice kernel varieties in Bangladesh. In Data in Brief (Vol. 57, p. 111109). Elsevier BV. https://doi.org/10.1016/j.dib.2024.111109
[5]D. Nair, K. Cohen, and M. Kumar, “Classification of Rice Using Genetic Fuzzy Cascading System,” Lecture Notes in Networks and Systems. Springer International Publishing, pp. 160–171, Sep. 30, 2022. doi: 10.1007/978-3-031-16038-7_17.
[6]Z. L. Lee and L. C. Tay, “Rice Grain Classification Using Convolution Neural Network with Small Dataset,” 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS). IEEE, Nov. 29, 2022. doi: 10.1109/scisisis55246.2022.10002003.
[7]P. Saxena, K. Priya, S. Goel, P. K. Aggarwal, A. Sinha, and P. Jain, “Rice varieties classification using machine learning algorithms,” Journal of Pharmaceutical Negative Results, vol. 13, no. 7, pp. 3762–3772, Dec. 2022, doi: https://doi.org/10.47750/pnr.2022.13.S07.479.
[8]M. J. Iqbal et al., “On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing,” Foods, vol. 12, no. 21. MDPI AG, p. 3993, Oct. 31, 2023. doi: 10.3390/foods12213993.
[9]M. Tasci, A. Istanbullu, S. Kosunalp, T. Iliev, I. Stoyanov, and I. Beloev, “An Efficient Classification of Rice Variety with Quantized Neural Networks,” Electronics, vol. 12, no. 10. MDPI AG, p. 2285, May 18, 2023. doi: 10.3390/electronics12102285.
[10]R. Setiawan and Hayatou Oumarou, “Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques,” Indonesian Journal of Data and Science, vol. 5, no. 1. Yocto Brain, pp. 54–63, Mar. 31, 2024. doi: 10.56705/ijodas.v5i1.129.
[11]M. M. Islam, G. M. S. Himel, M. G. Moazzam, and M. S. Uddin (2025b), “Rice Variety Classification Using Next Generation Convolutional Networks,” The Journal of Engineering, vol. 2025, no. 1. Institution of Engineering and Technology (IET), Jan. 2025. doi: 10.1049/tje2.70102
[12]Islam, Md. Masudul, et al. (2025c) “A Comprehensive Deep Learning Framework for Rice Variety Classification with Real-Time Deployment.” Smart Agricultural Technology, vol. 13, Elsevier, Dec. 2025, p. 101710, https://doi.org/10.1016/j.atech.2025.101710.
[13]C. S. Silva and U. Sonnadara, “Classification of Rice Grains Using Neural Networks,” Institute of Physics Sri Lanka (IPSL), vol. 29, Mar. 2013, Available: https://www.researchgate.net/publication/236733032_Classification_of_Rice_Grains_Using_Neural_Networks
[14]A. R. Pazoki, F. Farokhi, and Z. Pazoki, “Classification of rice grain varieties using two artificial neural networks (mlp and neuro-fuzzy),” The Journal of Animal and Plant Sciences, vol. 24, no. 1, pp. 336–343, Jan. 2014, Available: https://www.researchgate.net/publication/286168422_Classification_of_rice_grain_varieties_using_two_artificial_neural_networks_mlp
_and_neuro-fuzzy
[15]M. I. Hussain, M. Shovon, A. H. M. S. Parvez, M. Mamun, M. M. Hossain and S. H. Chowdhury, "A Comparative Study of CNN and Vision Transformer Methods for Rice Variety Classification with XAI," 2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 3899-3904, doi: 10.1109/ICCIT68739.2025.11490117