Plant Protect. Sci., X:X | DOI: 10.17221/33/2025-PPS

Burkholderia glumae in rice: Climate-driven epidemiology, advanced detection techniques and AI-enhanced predictive forecasting modelsReview

Hamood U Rehman ORCID...1,2, Norida Mazlan ORCID...1,3, Siti Izera Ismail ORCID...2,3, Nur Azura Husin ORCID...4
1 Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Selangor, Malaysia
2 Department of Plant Protection, Faculty of Agriculture, Universiti Putra Malaysia, Selangor, Malaysia
3 Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Selangor, Malaysia
3 Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Selangor, Malaysia

Bacterial panicle blight (BPB), caused by Burkholderia glumae, is a major and rising threat to global rice production. Its severity is heightened by climate change, with outbreaks driven by high temperatures and humidity during the vulnerable flowering stage. Current management is hampered by delayed detection and a disconnect between diagnostic tools and predictive models. This review synthesises advances across climatology, molecular biology, and computational agriculture to address this gap. We analyse climate-driven disease dynamics, precise molecular diagnostics, and the emergence of AI for real-time image-based detection and weather-based forecasting. The novel contribution of this work is the proposal of an Integrated Multimodal AI Framework (IMAF) that converges these domains. The IMAF links climate modelling, sensor data, and computer vision to enable proactive, climate-resilient disease forecasting and decision support. This synthesis represents a critical paradigm shift from reactive management to intelligent, predictive intervention. We conclude with a roadmap for developing and deploying such integrated systems to enhance global rice resilience.

Keywords: BPB prediction; artificial intelligence in agriculture; plant pathogen  forecasting; sustainable rice production

Received: March 3, 2025; Revised: February 14, 2026; Accepted: February 23, 2026; Prepublished online: September 8, 2026 

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