Agriculture plays a vital role in Indian economy. Among the cereals, Rice has shaped the culture, diet and economy of thousands of millions of people. The total Rice production in the world is 496.22 million metric tonnes as estimated by the United states Department of Agriculture in 2019 (USDA). India ranks second in rice production in the world with  the production of 115 million metric tones. In India, Rice productivity is low due to vagaries of monsoon, poor soil fertility, undulating topography, biotic stresses and lack of adoption of improved technologies. Among the biotic stresses insect pests constitute the key factor. In Telangana state, among the key insect pests of rice, Yellow stem borer (Scirpophaga incertulas) is one of the pests which causes major damage to the crop yields. In this study, three time series forecasting models, Artificial Neural Network (ANN), ARIMAX and ARIMAX-ANN Hybrid models were compared to forecast the damage caused by Yellow Stem borer (Scirpophaga incertulas) during both kharif and rabi seasons of Telangana state. To compare the effectiveness of these three models 30 years data both kharif and rabi seasons pertaining to Telangana state was used i.e., from 1990-2019. The results showed that the ARIMAX-ANN Hybrid model outperformed the ARIMAX and ANN Forecasting models.

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