A CNN-LSTM-Based Approach for the Early Detection of Rice Seed Diseases
Received: 24 July 2025 | Revised: 22 September 2025 | Accepted: 5 October 2025 | Online: 8 December 2025
Corresponding author: B. Nazia Hassan
Abstract
A novel approach is presented in this study, utilizing a Convolutional-Neural-Network Long Short Term-Memory (CNN-LSTM) model for detecting diseases in rice seeds. The CNN component was used for extracting features from seed images, while LSTM addressed the gradient descent problem. The model was evaluated using two open-access datasets containing both healthy and unhealthy samples. The analysis revealed that the proposed model achieved 98.4% accuracy, 98.01% precision, 98.7% recall, and 98.4% F1-score on the first dataset and 98.5% accuracy on the second dataset. These results were compared with previous works, and the highest performance was achieved in the CNN-LSTM model. The findings demonstrated the significant potential of the current model for practical applications in agricultural quality control and disease management, contributing to improved crop health and food security.
Keywords:
rice seed disease, CNN-LSTM, feature extraction, crop health, deep learningDownloads
References
N. K. Fukagawa and L. H. Ziska, "Rice: Importance for Global Nutrition," Journal of Nutritional Science and Vitaminology, vol. 65, no. Supplement, pp. S2–S3, 2019. DOI: https://doi.org/10.3177/jnsv.65.S2
S. Sundareswaran, P. Ray Choudhury, C. Vanitha, and D. K. Yadava, "Seed quality: Variety development to planting—An overview," in Seed Science and Technology: Biology, Production, Quality, Springer Nature Singapore Singapore, 2023, pp. 1–16. DOI: https://doi.org/10.1007/978-981-19-5888-5_1
N. A. Mohidem, N. Hashim, R. Shamsudin, and H. Che Man, "Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content," Agriculture, vol. 12, no. 6, May 2022, Art. no. 741. DOI: https://doi.org/10.3390/agriculture12060741
A. Gamage et al., "Role of organic farming for achieving sustainability in agriculture," Farming System, vol. 1, no. 1, Apr. 2023, Art. no. 100005. DOI: https://doi.org/10.1016/j.farsys.2023.100005
A. Arouna, I. A. Fatognon, K. Saito, and K. Futakuchi, "Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development," World Development Perspectives, vol. 21, Mar. 2021, Art. no. 100291. DOI: https://doi.org/10.1016/j.wdp.2021.100291
A. Bashir and S. Yuliana, "Identifying factors influencing rice production and consumption in Indonesia," Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan, vol. 19, no. 2, pp. 172–185, 2019. DOI: https://doi.org/10.23917/jep.v19i2.5939
O. Erenstein, M. Jaleta, K. Sonder, K. Mottaleb, and B. M. Prasanna, "Global maize production, consumption and trade: trends and R&D implications," Food Security, vol. 14, no. 5, pp. 1295–1319, May 2022. DOI: https://doi.org/10.1007/s12571-022-01288-7
M. Mallareddy et al., "Maximizing Water Use Efficiency in Rice Farming: A Comprehensive Review of Innovative Irrigation Management Technologies," Water, vol. 15, no. 10, May 2023, Art. no. 1802. DOI: https://doi.org/10.3390/w15101802
S. Bolan et al., "Impacts of climate change on the fate of contaminants through extreme weather events," Science of The Total Environment, vol. 909, Jan. 2024, Art. no. 168388. DOI: https://doi.org/10.1016/j.scitotenv.2023.168388
H. Takeshima and Y. Mano, "Intensification of rice farming: The role of mechanization and irrigation," in Rice Green Revolution in Sub-Saharan Africa, Berlin, Germany’: Springer, 2023, pp. 143–160. DOI: https://doi.org/10.1007/978-981-19-8046-6_7
R. Akhter and S. A. Sofi, "Precision agriculture using IoT data analytics and machine learning," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, Part B, pp. 5602–5618, Sept. 2022. DOI: https://doi.org/10.1016/j.jksuci.2021.05.013
Md. N. Mowla, N. Mowla, A. F. M. S. Shah, K. M. Rabie, and T. Shongwe, "Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey," IEEE Access, vol. 11, pp. 145813–145852, Dec. 2023, https://doi.org/10.1109/ACCESS.2023.3346299. DOI: https://doi.org/10.1109/ACCESS.2023.3346299
M. Ahmad, "Plant breeding advancements with ‘CRISPR-Cas’ genome editing technologies will assist future food security," Frontiers in Plant Science, vol. 14, Mar. 2023. DOI: https://doi.org/10.3389/fpls.2023.1133036
R. Li et al., "Predicting rice diseases using advanced technologies at different scales: present status and future perspectives," aBIOTECH, vol. 4, no. 4, pp. 359–371, Nov. 2023. DOI: https://doi.org/10.1007/s42994-023-00126-4
R. Shivappa et al., "Emerging Minor Diseases of Rice in India: Losses and Management Strategies," in Integrative Advances in Rice Research, London, UK: IntechOpen, 2021. DOI: https://doi.org/10.5772/intechopen.99898
B. N. Devanna et al., "Understanding the Dynamics of Blast Resistance in Rice-Magnaporthe oryzae Interactions," Journal of Fungi, vol. 8, no. 6, May 2022, Art. no. 584. DOI: https://doi.org/10.3390/jof8060584
S. Aggarwal et al., "Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business," Scientific Programming, vol. 2022, no. 1, 2022, Art. no. 1757888, https://doi.org/10.1155/2022/1757888. DOI: https://doi.org/10.1155/2022/1757888
Y. Guo et al., "Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning," Frontiers in Plant Science, vol. 12, Dec. 2021. DOI: https://doi.org/10.3389/fpls.2021.770916
M. A. Islam, Md. R. Hassan, M. Uddin, and M. Shajalal, "Germinative paddy seed identification using deep convolutional neural network," Multimedia Tools and Applications, vol. 82, no. 25, pp. 39481–39501, Mar. 2023. DOI: https://doi.org/10.1007/s11042-023-14914-z
R. Deng et al., "Automatic Diagnosis of Rice Diseases Using Deep Learning," Frontiers in Plant Science, vol. 12, Aug. 2021. DOI: https://doi.org/10.3389/fpls.2021.701038
B. Patel and A. Sharaff, "Rice Crop Disease Prediction Using Machine Learning Technique," International Journal of Agricultural and Environmental Information Systems (IJAEIS), vol. 12, no. 4, 2021. DOI: https://doi.org/10.4018/IJAEIS.20211001.oa5
G. Latif, S. E. Abdelhamid, R. E. Mallouhy, J. Alghazo, and Z. A. Kazimi, "Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model," Plants, vol. 11, no. 17, Aug. 2022, Art. no. 2230. DOI: https://doi.org/10.3390/plants11172230
S. Sengupta, A. Dutta, S. A. M. Abdelmohsen, H. A. Alyousef, and M. Rahimi-Gorji, "Development of a Rice Plant Disease Classification Model in Big Data Environment," Bioengineering, vol. 9, no. 12, Dec. 2022, Art. no. 758. DOI: https://doi.org/10.3390/bioengineering9120758
J. Cui and F. Tan, "Rice Plaque Detection and Identification Based on an Improved Convolutional Neural Network," Agriculture, vol. 13, no. 1, Jan. 2023, Art. no. 170. DOI: https://doi.org/10.3390/agriculture13010170
J. Li et al., "An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning," Plants, vol. 12, no. 18, Sept. 2023, Art. no. 3273. DOI: https://doi.org/10.3390/plants12183273
D. C. Trinh, A. T. Mac, K. G. Dang, H. T. Nguyen, H. T. Nguyen, and T. D. Bui, "Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection," AgriEngineering, vol. 6, no. 1, pp. 302–317, Feb. 2024. DOI: https://doi.org/10.3390/agriengineering6010018
D. Cheng, Z. Zhao, and J. Feng, "Rice Diseases Identification Method Based on Improved YOLOv7-Tiny," Agriculture, vol. 14, no. 5, Apr. 2024, Art. no. 709. DOI: https://doi.org/10.3390/agriculture14050709
Rice Plant Dataset.
Rice Leaf 5 diseases.
R. Kumar, G. Baloch, A. B. Buriro, and J. Bhatti, "Fungal blast disease detection in rice seed using machine learning," International Journal of Advanced Computer Science and Applications, vol. 12, no. 2, pp. 248–258, 2021. DOI: https://doi.org/10.14569/IJACSA.2021.0120232
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