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Indian Journal of Pure & Applied Biosciences (IJPAB)
Year : 2020, Volume : 8, Issue : 4
First page : (454) Last page : (461)
Article doi: : http://dx.doi.org/10.18782/2582-2845.8280

Plant Disease Forecasting Models

R. K. Nath^1*, K. H. Begum² and M. R. Choudhury^3
1Assistant  Professor, Department of Entomology, SCSCA, AAU, Dhubri, Assam
²District Consultant, NSFM, Tinsukia, Assam
³Junior Scientist, Regional Agricultural Research Station, AAU, Karimganj, Assam
*Corresponding Author E-mail: rupaknath09@gmail.com
Received: 11.07.2020  |  Revised: 17.08.2020   |  Accepted: 22.08.2020 

 ABSTRACT

Plant Disease forecasting means predicting the occurrence of a particular disease in a specified area ahead of time, so that suitable control measures can be undertaken in advance to avoid losses below economic threshold level. Prediction of a disease outbreak is based on assumptions about the pathogen's interactions with the host and environment - the disease triangle. The objective is to accurately predict when the three factors – host, environment and pathogen – all interact in such a manner that disease can occur and cause economic losses. Disease forecasting systems are different for Monocyclic and Polycyclic diseases and is   based on amount of initial inoculums, weather condition between the cropping seasons.Various forecasting models have been developed and utilized over the years for predicting various diseases across the globe. In 1978, a computerized forecasting system called FAST was developed for Alternaria solani, a fungus that infects tomato, when environmental conditions are favorable for early blight development.. BLITECAST, JHULSACAST etc are computerized forecast model for potato late blight. Predictions of disease outbreaks and the need for spraying have to be communicated rapidly to farmers.

Keywords: Forecasting, plant disease, BLITECAST, JHULSACAST

Full Text : PDF; Journal doi : http://dx.doi.org/10.18782

Cite this article: Nath, R.K., Begum, K.H., & Choudhury, M.R. (2020). Plant Disease Forecasting Models, Ind. J. Pure App. Biosci. 8(4), 454-461. doi: http://dx.doi.org/10.18782/2582-2845.8280