Epidemiology and Weather-driven Disease Progression of Major Rice Diseases in Telangana and Andhra Pradesh, India
S. Uday Krishna
Department of Plant Pathology, School of Agricultural Sciences, Malla Reddy University, Hyderabad – 500100, Telangana, India.
M. Surya Prakash Reddy
*
Department of Plant Pathology, School of Agricultural Sciences, Malla Reddy University, Hyderabad – 500100, Telangana, India.
Mahesh Palakuru
Department of Agricultural Engineering, School of Agricultural Sciences, Malla Reddy University, Hyderabad – 500100, India.
G. Jose Moses
Department of Computer Science, School of Engineering, Malla Reddy University, Hyderabad – 500100, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Rice (Oryza sativa L.) is a staple food for billions and a major crop in India, yet its productivity is significantly constrained by major fungal and bacterial diseases that can cause substantial yield and quality losses. Despite global advances, region-specific understanding of weather–disease interactions in key rice-growing states like Telangana and Andhra Pradesh remains limited, necessitating localized epidemiological analysis for effective disease management.
Aims: The study aims to quantify the incidence, severity, and disease progress dynamics (AUDPC) of five major rice diseases across Telangana and Andhra Pradesh and to establish statistically validated Pearson correlations between weather parameters and disease variables for each disease.
Study Design: Field survey and epidemiological study.
Place and Duration of Study: Six major rice-growing districts — Nalgonda, Khammam, Warangal, and Karimnagar in Telangana, and West Godavari and Krishna in Andhra Pradesh — during Kharif seasons of 2023, 2024, and 2025.
Methodology: Disease incidence (%), severity (PDI %), and AUDPC were assessed for Leaf Blast (Magnaporthe oryzae), Neck Blast (M. oryzae), Sheath Blight (Rhizoctonia solani AG1-IA), Bacterial Leaf Blight (Xanthomonas oryzaepv. oryzae), and False Smut (Ustilaginoidea virens) across 61 location-season records. Pearson's correlation was computed between six weather parameters and disease incidence, severity, and AUDPC.
Results: Sheath Blight recorded the highest mean incidence (38.38%) and AUDPC (389.7) in Kharif 2023, followed by Bacterial Leaf Blight (34.48%; AUDPC 342.6). A statistically significant (P < .05) declining trend was observed across all diseases from 2023 to 2025. Maximum Temperature was the dominant positive predictor for Bacterial Leaf Blight (r = +0.635, P = .015), Neck Blast (r = +0.650, P = .022), and Leaf Blast (r = +0.621, P = .031). False Smut was uniquely driven by Wind Speed (r = −0.812, P = .004) and Relative Humidity (r = +0.715, P = .020).
Conclusion: These statistically validated weather-disease relationships provide essential inputs for machine-learning-based early warning systems and precision drone spray scheduling for rice disease management.
Keywords: Rice diseases, Magnaporthe oryzae, Rhizoctonia solani, Xanthomonas oryzae, AUDPC, pearson correlation, disease epidemiology, Telangana, Andhra Pradesh.