A CNN-LSTM-based Approach for Classification and Quality Detection of Rice Varieties

Mavaddati, Samira; Razavi, Mohammad

doi:10.22044/jadm.2024.15282.2631

Document Type : Original/Review Paper

Authors

Samira Mavaddati ¹
Mohammad Razavi ²

¹ Electronic Department, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran.

² Computer Engineering Department, Shomal University, Amol, Iran.

https://doi.org/10.22044/jadm.2024.15282.2631

Abstract

Rice is one of the most important staple crops in the world and provides millions of people with a significant source of food and income. Problems related to rice classification and quality detection can significantly impact the profitability and sustainability of rice cultivation, which is why the importance of solving these problems cannot be overstated. By improving the classification and quality detection techniques, it can be ensured the safety and quality of rice crops, and improving the productivity and profitability of rice cultivation. However, such techniques are often limited in their ability to accurately classify rice grains due to various factors such as lighting conditions, background, and image quality. To overcome these limitations a deep learning-based classification algorithm is introduced in this paper that combines the power of convolutional neural network (CNN) and long short-term memory (LSTM) networks to better represent the structural content of different types of rice grains. This hybrid model, called CNN-LSTM, combines the benefits of both neural networks to enable more effective and accurate classification of rice grains. Three scenarios are demonstrated in this paper include, CNN, CNN in combination with transfer learning technique, and CNN-LSTM deep model. The performance of the mentioned scenarios is compared with the other deep learning models and dictionary learning-based classifiers. The experimental results demonstrate that the proposed algorithm accurately detects different rice varieties with an impressive accuracy rate of over 99.85%, and 99.18% to identify quality for varying combinations of rice varieties with an average accuracy of 99.18%.

Keywords

Main Subjects

I.3.6. Electronics

References

[1] L. Tang, R. Hamdulla, C. Rong, H. Qinan, P. Xiaoya, H. Yaxin, and Z. Guoyou, "Food security in China: a brief view of rice production in recent 20 years," Foods, vol. 11, no. 21, pp. 3324, 2022.

[2] A. S. Hamzah and A. Mohamed, "Classification of white rice grain quality using ANN: a review," IAES Int. J. Artif. Intell., vol. 9, no. 4, pp. 600, 2020.

[3] J. F. S. Gomes and F. R. Leta, "Applications of computer vision techniques in the agriculture and food industry: a review," Eur. Food Res. Technol., vol. 235, pp. 989-1000, 2012. doi: 10.1007/s00217-012-1844-2.

[4] V. Filipović, M. Panić, S. Brdar, and B. Brkljač, "Significance of Morphological Features in Rice Variety Classification Using Hyperspectral Imaging," in Proc. 12th Int. Symp. Image Signal Process. Anal. (ISPA), Zagreb, Croatia, 2021, pp. 171-176. doi: 10.1109/ISPA52656.2021.9552086.

[5] S. B. Ahmed, S. F. Ali, and A. Z. Khan, "On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review," IEEE Access, vol. 9, pp. 160779-160796, 2021. doi: 10.1109/ACCESS.2021.3130472.

[6] M. Koklu, I. Cinar, and Y. S. Taspinar, "Classification of rice varieties with deep learning methods," Comput. Electron. Agric., vol. 187, 2021.

[7] P. R. Jeyaraj, S. P. Asokan, and E. R. Nadar, "Computer-Assisted Real-Time Rice Variety Learning Using Deep Learning Network," Rice Sci., vol. 29, no. 5, pp. 489-498, 2022. doi: 10.1016/j.rsci.2022.02.003.

[8] S. Pradana-López, A. M. Pérez-Calabuig, C. Rodrigo, M. A. Lozano, J. C. Cancilla, and J. S. Torrecilla, "Low requirement imaging enables sensitive and robust rice adulteration quantification via transfer learning," Food Control, vol. 127, 2021. doi: 10.1016/j.foodcont.2021.108122.

[9] I. Golpour, J. Amiri Parian, and R. A. Chayjan, "Identification and classification of bulk paddy, brown, and white rice cultivars with color features extraction using image analysis and neural network," Network, vol. 10, 2014.

[10] P. Shrivastava, K. Singh, and A. Pancham, "Classification of grains and quality analysis using deep learning," Int. J. Eng. Adv. Technol., vol. 11, no. 1, pp. 244-250, 2021. doi: 10.35940/ijeat.A3213.1011121.

[11] Y. Abbaspour-Gilandeh, A. Molaee, S. Sabzi, N. Nabipur, S. Shamshirband, and A. Mosavi, "A combined method of image processing and artificial neural network for the identification of 13 Iranian rice cultivars," Agronomy, vol. 10, no. 1, p. 117, 2020. doi: 10.3390/agronomy10010117.

[12] R. Deng, "Deep learning-based automatic detection of productive tillers in rice," Comput. Electron. Agric., vol. 177, 2020. doi: 10.1016/j.compag.2020.105703.

[13] A. R. Pazoki, F. Farokhi, and Z. Pazoki, "Classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy)," J. Anim. Plant Sci., vol. 24, pp. 336-343, 2014.

[14] I. Chatnuntawech, K. Tantisantisom, P. Khanchaitit, T. Boonkoom, B. Bilgic, and E. Chuangsuwanich, "Rice classification using spatio-spectral deep convolutional neural network," Comput. Vis. Pattern Recognit. Cornell Univ., vol. 3, no. 1, pp. 1-22, 2018.

[15] R. Ahmad Dar, N. Din, M. Bhat, A. Assad, Z. Islam, W. Gulzar, and A. Yaseen, "Classification of rice grain varieties using deep convolutional neural network architectures," SSRN Electron. J., 2022.

[16] P. Vaibhav Amit and V. J. Manjunath, "Convolutional neural network with transfer learning for rice type classification," in Proc. SPIE 10696, Tenth Int. Conf. Mach. Vis. (ICMV), 2018, p. 1069613.

[17] H. Bichri, A. Chergui, and M. Hain, "Image Classification with Transfer Learning Using a Custom Dataset: Comparative Study," Procedia Comput. Sci., vol. 220, pp. 48-54, 2023.

[18] Y. Tatsunami and M. Taki, "Sequencer: Deep LSTM for image classification," arXiv preprint, vol. arXiv:2205.01972, 2022.

[19] V. Lakshmi and K. Seetharaman, "Rice Classification and Quality Analysis using Deep Neural Network," in Proc. Int. Conf. Intell. Innov. Eng. Technol. (ICIIET), Coimbatore, India, 2022, pp. 307-314. doi: 10.1109/ICIIET55458.2022.9967584.

[20] N. Hong and N. Thai-Nghe, "Deep learning for rice quality classification," in Proc. Int. Conf. Adv. Comput. Appl. (ACOMP), Nha Trang, Vietnam, 2019, pp. 92-96. doi: 10.1109/ACOMP.2019.00021.

[21] S. Mavaddati, "Rice classification and quality detection based on sparse coding technique," Int. J. Eng. Trans. B Appl., vol. 31, no. 11, pp. 1910-1917, 2018. doi: 10.5829/ije.2018.31.11b.15.

[22] S. Mavaddati and S. Mavaddati, "Rice Classification with Fractal-based Features based on Sparse Structured Principal Component Analysis and Gaussian Mixture Model," Journal of Artificial Intelligence & Data Mining (JAIDM), vol. 9, no. 2, pp. 235-244, 2021. doi: 10.22044/jadm.2021.9583.2090.

[23] A. P. Shaji and H. S., "Data augmentation for improving rice leaf disease classification on residual network architecture," in Proc. Int. Conf. Adv. Comput. Commun. Appl. Inform. (ACCAI), Chennai, India, 2022, pp. 1-7. doi: 10.1109/ACCAI53970.2022.9752495.

[24] Z. Liu, Y. K. Cao, Y. J. Li, X. X. Xiao, Q. C. Qiu, Y. Zhao, M. J., and L. Z. Cui, "Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network," Comput. Methods Programs Biomed., vol. 187, 2020.

[25] Z. Mushtaq, S. F. Su, and Q. V. Tran, "Spectral images based environmental sound classification using CNN with meaningful data augmentation," Appl. Acoust., vol. 172, 2021.

[26] Z. Zhong, L. Zheng, G. Kang, S. Yang, and Y. Li, "Random erasing data augmentation," in Proc. 34th AAAI Conf. Artif. Intell., New York, NY, USA, 2020, pp. 13001-13008.

[27] R. Yakkundimath, G. Saunshi, B. Anami, and S. Palaiah, "Classification of rice diseases using convolutional neural network models," J. Inst. Eng. (India): B, vol. 103, no. 4, pp. 1047-1059, 2022.

[28] N. Prakash, R. Rajakumar, N. L. Madhuri, M. Jyothi, A. P. Bai, M. Manjunath, and K. Gowthami, "Image classification for rice varieties using deep learning models," YMER Digit, vol. 21, pp. 261-275.

[29] P. Madan, V. Singh, D. P. Singh, M.Diwakar, B. Pant, and A. Kisho, "A hybrid deep learning approach for ECG-based arrhythmia classification," Bioengineering, vol. 9, no. 4, 2022.

[30] J. Amin, M. Sharif, and M. Raza, "Brain tumor detection: a long short-term memory (LSTM)-based learning model," Neural Comput & Applic, vol. 32, pp. 15965-15973, 2020.

[31] A. Begum, V. D. Kumar, J. Asghar, D. Hemalatha, and G. Arulkumaran, "A combined deep CNN-LSTM with a random forest approach for breast cancer diagnosis," Complexity, pp. 1-9, 2022.

[32] Y. Gao, K. M. Mosalam, "Deep transfer learning for image-based structural damage recognition," Comput. Aided Civ. Infrastruct. Eng., vol. 33, pp. 748-768, 2018.

[33] S. Kentsch, M. L. Lopez Caceres, D. Serrano, F. Roure, Y. Diez, "Computer vision and deep learning techniques for the analysis of drone-acquired forest images: a transfer learning study," Remote Sens., vol. 12, 2020.

[34] G. Mingyu, Q. Dawei, M. Hongbo, and Jianfeng, C. "A transfer residual neural network based on Resnet-34 for detection of wood knot defects. Forests," vol. 12, no.2, 2021. doi: 10.3390/f12020212.

[35] A. Mehra, A. Bhati, A. Kumar, and R. Malhotra, "Skin cancer classification through transfer learning using ResNet-50," Emerging technologies in data mining and information security, pp-55-62, 2021.10.1007/978-981-33-4367-2-6.

[36] D. Kingma, J. Ba, "Adam: a method for stochastic optimization," Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.

[37] P. Harar, J. B. Alonso-Hernandezy, J. Mekyska, Z. Galaz, R. Burget, Z. Smekal, "Voice pathology detection using deep learning: a preliminary study," International Conference and Workshop on Bioinspired Intelligence (IWOBI), 1-4, 2017.

[38] A. Sharma, A. Nandal, A. Dhaka, L. Zhou, A. Alhudhaif, F. Alenezi, K. Polat, "Brain tumor classification using the modified ResNet50 model based on transfer learning," Biomedical Signal Processing and Control, vol. 86, 2023. 105299. 10.1016/j.bspc.2023.105299.

[39] J. Demsar, Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, vol. 7, pp.1-30, 2006.