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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Development of Wheat yield forecasting model using different statistical technique and artificial neural network approach for Pratapgarh region |
| Authors Name: | Ravi kushwaha , Dr. Shweta Gautam |
| Unique Id: | IJSDR2211021 |
| Published In: | Volume 7 Issue 11, November-2022 |
| Abstract: | The present study investigated the influence of weather variables on crop yield forecasting. Weather variables play an important role in development and growth of crops. The yield data of Wheat has been taken from the Directorate of Economics and Statistics, Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture and Farmers Welfare for time 1991-2019 for Pratapgarh districts. In this study, the focus was on the development of multivariate meteorological yield models through stepwise linear regression technique using weather variables and historic crop yield. The model use, maximum and minimum temperature, rainfall and relative humidity during crop growing period. For the validation part, the statistical equation developed from the yield and weather data of 1991-2015. Yield prediction was carried out for Wheat (Triticum aestivum) in Pratapgarh districts for 2016 to 2019 year. From the multivariate meteorological yield models, it can be inferred that among all the weather variables, temperature (maximum & minimum), rainfall and relative humidity play key role as predictor in Pratapgarh districts. Further the ANN models have been experimented using different partitions of training patterns and different combinations of weather parameters. Experiments have also been conducted for different number of neurons in hidden layer and wheat yield forecasts for the period 2015-16, 2016-17, 2017-18, 2018-19 and 2019-20 have been obtained. The performance of different methods on yield forecasting models has been compared based on different statics viz.., NRMSE (Normalized Root Mean Square Error), MAPE (Mean Absolute Percentage Error) and R2. |
| Keywords: | Wheat, Weather variables, Crop yield prediction, Statistical model, ANN |
| Cite Article: | "Development of Wheat yield forecasting model using different statistical technique and artificial neural network approach for Pratapgarh region", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 11, page no.120 - 130, November-2022, Available :http://www.ijsdr.org/papers/IJSDR2211021.pdf |
| Downloads: | 000337074 |
| Publication Details: | Published Paper ID: IJSDR2211021 Registration ID:202434 Published In: Volume 7 Issue 11, November-2022 DOI (Digital Object Identifier): Page No: 120 - 130 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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