Major diseases of rice in Asia: Occurrence, impact, and management strategies with special focus on south Asia
DOI:
https://doi.org/10.26832/24566632.2026.1102017Keywords:
Integrated disease management, Magnaporthe oryzae, Rice diseases, South Asia, Xanthomonas oryzaeAbstract
Rice (Oryza sativa L.) is the principal staple food of South Asia, supporting the livelihoods and nutrition of over 1.8 billion people across India, Bangladesh, Nepal, Pakistan, and Sri Lanka. Despite its critical importance, rice production in the region is persistently undermined by a wide spectrum of diseases caused by fungi, bacteria, viruses, and nematodes, which collectively cause yield losses ranging from 10 to 80 percent annually. This review aims to synthesize the current status, epidemiology, economic impact, and management of the major rice diseases of South Asia, and to highlight the role of emerging artificial intelligence (AI) tools for
sustainable disease control. Ten major diseases were classified into four causal-agent groups, and seven of greatest economic significance were analyzed in detail Rice Blast (Magnaporthe oryzae), Bacterial Leaf Blight (Xanthomonas oryzae pv. oryzae), Sheath Blight (Rhizoctonia solani), Brown Spot (Bipolaris oryzae), False Smut (Ustilaginoidea virens), Bakanae (Fusarium fujikuroi) and Rice Tungro Virus. Maximum reported yield losses were 80% for Rice Tungro, 70% for Rice Blast, 60% for Bacterial Leaf Blight, 50% for Sheath Blight and 58% for Brown Spot, with an estimated regional economic burden of US$ 4–10 billion per year. Thus, integration of host resistance with AI-assisted, edge-deployable diagnostic systems offers a region-specific and climate-adaptive pathway for sustainable rice disease management in South Asia, distinct from the chemical-centric approaches that have so far dominated the region.
Downloads
References
Adhikari, A., Sapkota, A., Regmi, P., Neupane, S., Sapkota, S., Ghimire, S., & Kandel, B. P. (2019). Effect of establishment method and different weed management practices on dry direct seeded rice (DDSR) at Rampur, Chitwan. Journal of Agricultural Science and Engineering, 1(3), 332-344. https://jase.samipubco.com/article_179754.html
Amruta, N., Prasanna Kumar, M. K., Kandikattu, H. K., Sarika, G., Puneeth, M. E., Ranjitha, H. P., & Narayanaswamy, S. (2019). Bio-priming of rice seeds with novel bacterial strains, for management of seed borne Magnaporthe oryzae L. Plant Physiology Reports, 24(4), 507-520. https://doi.org/10.1007/s40502-019-00492-6
Barbedo, J. G. A. (2018). Factors influencing the use of deep learning for plant disease recognition. Biosystems Engineering, 172, 84–91. https://doi.org/10.1016/j.biosystemseng.2018.05.013
Bonman, J. M. (1992). Durable resistance to rice blast disease-environmental influences. Euphytica, 63(1), 115-123. https://doi.org/10.1007/BF00023917
Chakraborty, S., & Newton, A. C. (2011). Climate change, plant diseases and food security: an overview. Plant pathology, 60(1), 2-14. https://doi.org/10.1111/j.1365-3059.2010.02411.x
FAO. (2023). FAOSTAT: Food and Agriculture Data. Statistics Division, Food and Agriculture Organization of the United Nations, Rome. fao.org/faostat/en/
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
Ghimire, S., Lamsal, R., Sankuratri, A., Lakkarsu, P., & Vutla, S. (2026). Artificial intelligence-driven plant disease detection and diagnosis: A comprehensive review of deep learning approaches, multimodal sensing technologies, and future perspectives in precision agriculture. The Plant Pathology Journal, 42(2), 121. https://doi.org/10.5423/PPJ.RW.01.2026.0004
GoP. (2022). Agricultural statistics of Pakistan 2021–22. Ministry of National Food Security and Research, Government of Pakistan, Islamabad. https://www.pbs.gov.pk/wp-content/uploads/2020/07/merged_pdf.pdf
Hibino, H. (1996). Biology and epidemiology of rice viruses. Annual Review of Phytopathology, 34(1), 249–274. https://doi.org/10.1146/annurev.phyto.34.1.249
Howard, R. J., & Valent, B. (1996). Breaking and entering: host penetration by the fungal rice blast pathogen Magnaporthe grisea. Annual Reviews in Microbiology, 50(1), 491-512. https://doi.org/10.1146/annurev.micro.50.1.491
IRRI. (2022). World rice statistics online query facility. International Rice Research Institute, Los Baños, Philippines. http://ricestat.irri.org
Kotamraju, V. K. K. (2010). Management of sheath blight and enhancement of growth and yield of rice with plant growth-promoting rhizobacteria. Auburn University. https://etd.auburn.edu/handle/10415/2381
Ladhalakshmi, D., Laha, G. S., Singh, R., Karthikeyan, A., Mangrauthia, S. K., Sundaram, R. M., & Viraktamath, B. C. (2012). Isolation and characterization of Ustilaginoidea virens and survey of false smut disease of rice in India. Phytoparasitica, 40(2), 171-176. https://doi.org/10.1007/s12600-011-0214-0
Lamsal, R., Ghimire, S., Yadav, R., & Manandhar, H. K. (2024). Response of Nepalese rice landraces to brown spot [Bipolaris oryzae (Breda de Haan) Shoemaker] at Rampur, Chitwan, Nepal. Agronomy Journal of Nepal, 8, 203-212. https://doi.org/10.3126/ajn.v8i1.70892
Mahlein, A. K., Kuska, M. T., Behmann, J., Polder, G., & Walter, A. (2018). Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annual Review of Phytopathology, 56(1), 535–558. https://doi.org/10.1146/annurev-phyto-080417-050100
Mew, T. W., Leung, H., Savary, S., Vera Cruz, C. M., & Leach, J. E. (2004). Looking ahead in rice disease research and management. Critical Reviews in Plant Sciences, 23(2), 103-127. https://doi.org/10.1080/07352680490433231
MoALD. (2022). Statistical information on Nepalese agriculture 2020/21. Ministry of Agriculture and Livestock Development, Government of Nepal, Kathmandu. https://centralaglab.gov.np/sites/default/files/publication-file/STATISTICAL-INFORMATION-ON-NEPALESE-AGRICULTURE-2077-78-82136.pdf
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Niño-Liu, D. O., Ronald, P. C., & Bogdanove, A. J. (2006). Xanthomonas oryzae pathovars: model pathogens of a model crop. Molecular Plant Pathology, 7(5), 303-324. https://doi.org/10.1111/j.1364-3703.2006.00344.x
Ou, S. H. (1985). Rice diseases (2nd ed.). International Rice Research Institute, Los Baños, Philippines. pp 380. https://books.google.co.in/books/about/Rice_Diseases.html?id=-k3mewv9nMoC&redir_esc=y
Padmanabhan, S. Y. (1973). The great Bengal famine. Annual Review of Phytopathology, 11(1), 11–26. https://doi.org/10.1146/annurev.py.11.090173.000303
Savary, S., Willocquet, L., Elazegui, F. A., Castilla, N. P., & Teng, P. S. (2000). Rice pest constraints in tropical Asia: quantification of yield losses due to rice pests in a range of production situations. Plantdisease, 84(3), 357-36. https://doi.org/10.1094/PDIS.2000.84.3.357
Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3(3), 430–439. https://doi.org/10.1038/s41559-018-0793-y
Sharma, P., Bora, L. C., Puzari, K. C., Baruah, A. M., Baruah, R., Talukdar, K., & Phukan, A. (2017). Review on bacterial blight of rice caused by Xanthomonas oryzae pv. oryzae: different management approaches and role of Pseudomonas fluorescens as a potential biocontrol agent. International Journal of Current Microbiology and Applied Science, 6(3), 982-1005. https://doi.org/10.20546/ijcmas.2017.603.117
Singh, R., Sunder, S., & Kumar, P. (2016). Sheath blight of rice: current status and perspectives. Indian Phytopathology, 69(4), 340-351.
Singh, S. (2013). Epidemiology and management of blast disease of rice. Punjab Agricultural University, Ludhiana, India, Master’s thesis, Punjab Agricultural University. https://krishikosh.egranth.ac.in/items/f0ee4206-d615-4022-876f-80f9951ae934
Skamnioti, P., & Gurr, S. J. (2009). Against the grain: safeguarding rice from rice blast disease. Trends in Biotechnology, 27(3), 141-150. https://doi.org/10.1016/j.tibtech.2008.12.002
Vera Cruz, C. M., Bai, J., Ona, I., Leung, H., Nelson, R. J., Mew, T. W., & Leach, J. E. (2000). Predicting durability of a disease resistance gene based on an assessment of the fitness loss and epidemiological consequences of a virulence gene mutation. Proceedings of the National Academy of Sciences, 97(25), 13500-13505. https://doi.org/10.1073/pnas.25027199
Yadav, R. N., Mishra, D., Zaidi, N. W., Singh, U. S., & Singh, H. B. (2018). Bio-control efficacy of Trichoderma spp. against the major diseases of rice (Oryzae sativa L.). International Journal of Agriculture, Environment and Biotechnology, 11(3), 543-548. https://doi.org/10.30954/0974-1712.06.2018.17
Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., González-Moreno, P., Ma, H., Ye, H., & Sobeih, T. (2019). A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing, 11(13), 1554. https://doi.org/10.3390/rs11131554
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Agriculture and Environmental Science Academy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
