Rice Blast Forecasting Using Interval Valued Data at Coimbatore, India

N. Sandeep

Department of Physical Science and Information Technology, AEC and RI, TNAU, India.

S. G. Patil *

Department of Physical Science and Information Technology, AEC and RI, TNAU, India.

C. Gopalakrishnan

Department of Plant Pathology, TNAU, India.

M. Vijayabhama

Department of Physical Science and Information Technology, AEC and RI, TNAU, India.

Ga. Dheebakaran

Department of Agro Climate Research Center (ACRC), TNAU, India.

Meena A. G.

Department of Plant Pathology, TNAU, India.

*Author to whom correspondence should be addressed.


Aims: The persistence of rice blast, caused by the fungus Magnaporthe oryzae, continues to pose a significant threat to rice production worldwide, impacting both yields and food security. The primary goal of this study is to apply interval-valued independent weather data to accurately model the dependent variable of percentage disease incidence.

Study Design: In this paper, we present a detailed study on forecasting rice blast outbreaks through the application of Average method, Center method and Min Max method using interval valued weather data and percentage disease incidence.

Place and Duration of Study: The blast disease data include percent disease incidence (PDI) collected at the Paddy Breeding Station (PBS), Tamil Nadu Agricultural University, Coimbatore, from 2018 to 2021.And Weather variables includes the following: Maximum Temperature, Minimum Temperature, Relative humidity (morning), Relative humidity (evening) from 2018 to 2021.

Methodology: The available interval weather parameter data and disease incidence data are utilized to fit a regression model, specifically employing simple linear regression and multiple linear regression, in the R version 4.3.0.

Results: Upon analyzing various methods, it is evident that the variables of Minimum temperature exhibit a significant relationship with a high level of significance, indicating a significance level at

 P 0.001.

Conclusion: Minimum temperature shows more contribution in disease incidence followed by relative humidity at evening.

Keywords: Rice blast, interval valued data, average method, center method, min max method

How to Cite

Sandeep, N., Patil, S. G., Gopalakrishnan, C., Vijayabhama, M., Dheebakaran, G., & A. G., M. (2023). Rice Blast Forecasting Using Interval Valued Data at Coimbatore, India. International Journal of Environment and Climate Change, 13(10), 1882–1888. https://doi.org/10.9734/ijecc/2023/v13i102844


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Pattanayak S, Das S, Outbreak of rice blast on the coastal region of South-Eastern India. International Journal of Agriculture, Environment and Biotechnology. 2020;13(1):59-70.

Chʻiu J-c. Rice diseases; proceedings. in Tao tso ping hai chuan ti yen tao hui (1969: Taipei). 1971. Joint Commission on Rural Reconstruction.

Sakulkoo W et al., A single fungal MAP kinase controls plant cell-to-cell invasion by the rice blast fungus. Science. 2018. 359(6382):1399-1403.

Kirtphaiboon S et al., Model of rice blast disease under tropical climate conditions. Chaos, Solitons & Fractals. 2021;143: 110530.

Malicdem AR, Fernandez PL. Rice blast disease forecasting for northern Philippines. WSEAS Trans. Inf. Sci. Appl, 2015;12:120-129.

Garcia-Ascanio C, Maté C. Electric power demand forecasting using interval time series: A comparison between VAR and iMLP. Energy policy. 2010.38(2): 715-725.

Maia ALS, de Carvalho FdA, Ludermir TB. Forecasting models for interval-valued time series. Neurocomputing, 2008. 71(16-18): p. 3344-3352.

Sun Y et al., Model averaging for interval-valued data. European Journal of Operational Research. 2022;301(2):772-784.

Kumar A. Forewarning Models for Alternaria blight in mustard crop. 2013.

SAHA M. Forecasting of rice blast disease severity in West Bengal, India based on PDI values and Cumulative logit model.

Neto EdAL De FDA Carvalho, Centre and range method for fitting a linear regression model to symbolic interval data. Computational Statistics & Data Analysis. 2008;52(3):1500-1515.

Billard L, Diday E. Regression analysis for interval-valued data, in Data analysis, classification, and related methods., Springer. 2000;369-374.

Chacón JE, Rodríguez O. Regression models for symbolic interval-valued variables. Entropy. 2021;23(4):429.