National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Agriculture is the most important sector of Indian Economy. Indian agriculture sector accounts for 18 per cent of India's gross domestic product (GDP) and provides employment to 50% of the countries workforce. Among cereals, Rice (Oryza sativa I.) is the most important cereal crop of the world both in respect to area and production. The total Rice production in the world is 496.22 million metric tons as estimated by the United states Department of Agriculture in April 2020 (USDA). India ranks second in rice production in the world with the production of 116.42 million metric tons. Certain biotic, abiotic reasons are resulting in low productivity. Among the biotic stresses insect pests constitute the key factor. In Telangana state, among the key insect pests of rice, Brown planthopper (Nilaparvata lugens) is one of the pests which cause 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 Brown Planthopper (Nilaparvata lugens) during both kharif and rabi seasons of Telangana state. To compare the effectiveness of these three models 30 years data during 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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