Estimation of Disease Severity and Screening of Sheath BlightResistant ‎Cultivar among Indigenous Rice Genotypes

  • Authors

    • Kumar Avinash Biswal Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University ‎of Technology and Management, Paralakhemundi, Odisha. -761211, India
    • Siddhartha Das Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University ‎of Technology and Management, Paralakhemundi, Odisha. -761211, India https://orcid.org/0000-0002-7482-2555
    • Bikramjeet Ghose Department of Agricultural Economics and Statistics, Centurion University of Technology and ‎ Management, Paralakhemundi, Odisha, India
    • Soumik Ray Department of Agricultural Economics and Statistics, Centurion University of Technology and ‎ Management, Paralakhemundi, Odisha, India
    • Nirakar Ranasingh Department of Plant Pathology, College of Agriculture, OUAT, Bhawanipatna
    • Rajeeb Lochan Moharana Department of Seed Science and Technology, College of Agriculture, OUAT, Bhawanipatna
    • Arnab Adhikary Department of Genetics and Plant Breeding, M. S. Swaminathan School of Agriculture, Centurion ‎ University of Technology and Management, Paralakhemundi, Odisha. -761211, India
    https://doi.org/10.14419/qs180j42

    Received date: July 29, 2025

    Accepted date: September 9, 2025

    Published date: October 12, 2025

  • ANN (Artificial Neural Network); AUDPC; Gompertz Model; RF (Random Forest) and Sheath ‎Blight

  • Abstract

    Sheath blight of rice caused by Rhizoctonia solani is a major disease that causes substantial yield loss ‎of production globally. Development and use of resistant cultivars is a cost-effective strategy to ‎manage the disease. The present study encompasses 43 rice germplasms that were evaluated for sheath ‎blight resistance during 2022-2024 in Ranadevi farm of Centurion University of Technology and ‎Management, Paralakhemundi, Odisha. Evaluation was conducted under natural field conditions ‎supplemented with artificial inoculation of the pathogen. Disease severity was measured using a 0-5 ‎disease grading scale. The classification of genotypes was carried out using Percent Disease Index ‎‎(PDI), Area Under Disease Progress Curve (AUDPC), and Genotypic Category (GC). Disease ‎modeling was established through the Gompertz model, RF (Random Forest), and ANN (Artificial ‎Neural Network) models. Some of the genotypes like Swarna Subhagya, CR 1017, and NLR 33892 ‎indicated consistent resistance or moderate resistance between seasons, having lower AUDPC ‎value (<400), whereas, number of famous varieties (>15), such as IR 64, MTU 1010 and CR Dhan ‎‎308, recorded high susceptibility with AUDPC of more than 1500 and GC score of 5. Also, disease ‎progression was well fitted within the different categories of resistance (R2 = 0.9747 - 0.9963) ‎using the Gompertz model where whereas in the machine learning model, resistance responses were ‎categorized. Further, compared to the test accuracy of the (Random Forest) RF classifier, which was ‎‎76.92 per cent, the ANN (Artificial Neural Network) model produced an accuracy of 81 per cent‎.

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  • How to Cite

    Biswal, K. A. ., Das, S., Ghose, B., Ray, S., Ranasingh, N., Moharana, R. L., & Adhikary, A. (2025). Estimation of Disease Severity and Screening of Sheath BlightResistant ‎Cultivar among Indigenous Rice Genotypes. International Journal of Basic and Applied Sciences, 14(6), 188-221. https://doi.org/10.14419/qs180j42