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http://krishi.icar.gov.in/jspui/handle/123456789/71257
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gayathri Chitikela , Meena Admala, Vijaya Kumari Ramalingareddy, Nirmala Bandumula , Gabrijel Ondrasek , Raman Meenakshi Sundaram and Santosha Rathod | en_US |
dc.date.accessioned | 2022-04-07T05:23:24Z | - |
dc.date.available | 2022-04-07T05:23:24Z | - |
dc.date.issued | 2021-09-18 | - |
dc.identifier.citation | Chitikela, Gayathri, Meena Admala, Vijaya K. Ramalingareddy, Nirmala Bandumula, Gabrijel Ondrasek, Raman M. Sundaram, and Santosha Rathod. 2021. "Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India" Agronomy 11, no. 9: 1878. | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/71257 | - |
dc.description | Not Available | en_US |
dc.description.abstract | This study’s objective was to assess the impact of the COVID-19 pandemic on tomato supply and prices in Gudimalkapur market in Hyderabad, India. The lockdown imposed by the government of India from 25 March 2020 to 30 June 2020 particularly affected the supply chain of perishable agricultural products, including tomatoes as one of the major vegetable crops in the study area. The classical time series models such as autoregressive integrated moving average (ARIMA) intervention models and artificial intelligence (AI)-based time-series models namely support vector regression (SVR) intervention and artificial neural network (ANN) intervention models were used to predict tomato supplies and prices in the studied market. The modelling results show that the pandemic had a negative impact on supply and a positive impact on tomato prices. Moreover, the ANN intervention model outperformed the other models in both the training and test data sets. The superior performance of the ANN intervention model could be due to its ability to account for the nonlinear and complex nature of the data with exogenous intervention variable. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | intervention; artificial intelligence; COVID-19 pandemic; ARIMA; SVR; ANN | en_US |
dc.title | Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Agronomy | en_US |
dc.publication.volumeno | 11 (9) | en_US |
dc.publication.pagenumber | 1878 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https://doi.org/10.3390/agronomy11091878 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Institute of Rice Research | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
Appears in Collections: | CS-IIRR-Publication |
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