TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand
Abstract To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficult...
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| Format: | Article |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80488-x |
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| author | Samarth Godara Shbana Begam Ram Swaroop Bana Jatin Bedi Rajni Jain Md. Ashraful Haque Rajender Parsad Sudeep Marwaha Madhu Patial Saber Shirzad Ravi Nirmal |
| author_facet | Samarth Godara Shbana Begam Ram Swaroop Bana Jatin Bedi Rajni Jain Md. Ashraful Haque Rajender Parsad Sudeep Marwaha Madhu Patial Saber Shirzad Ravi Nirmal |
| author_sort | Samarth Godara |
| collection | DOAJ |
| description | Abstract To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers’ demand for assistance in India. The presented study uses the data from the Kisan Call Centres, a nationwide network of farmer’s helplines, including 28.6 million call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. Additionally, the extracted insights are presented in the form of “Topic-wise Problems’ Trend Clusters” (TPTC), which can be used by policymakers in both the government and private sectors to aid decision-making. The article also introduces a pipeline for designing forecasting models to estimate the monthly frequency of farmer inquiries (in terms of the number of query calls). The seven statistical forecasting models were examined in the study with the TBATP1 (Trigonometric seasonal components with Box-Cox transformation incorporating ARIMA errors and Trend including the Seasonal components) model attaining the lowest error rates in terms of Root Mean Square Error (0.034) and Mean Absolute Error (0.107). The study also explores numerous applications of the derived insights in the real world as well as the future scope of the presented work. |
| format | Article |
| id | doaj-art-e9f0ae66622b4ff1a7ca851a4b5808dd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e9f0ae66622b4ff1a7ca851a4b5808dd2024-12-01T12:18:40ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-80488-xTPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demandSamarth Godara0Shbana Begam1Ram Swaroop Bana2Jatin Bedi3Rajni Jain4Md. Ashraful Haque5Rajender Parsad6Sudeep Marwaha7Madhu Patial8Saber Shirzad9Ravi Nirmal10ICAR-Indian Agricultural Statistics Research InstituteICAR-National Institute for Plant BiotechnologyICAR- Indian Agricultural Research InstituteThapar Institute of Engineering And TechnologyICAR-National Institute of Agricultural Economics and Policy ResearchICAR-Indian Agricultural Statistics Research InstituteICAR-Indian Agricultural Statistics Research InstituteICAR-Indian Agricultural Statistics Research InstituteICAR- Indian Agricultural Research InstituteICAR- Indian Agricultural Research InstituteICAR- Indian Agricultural Research InstituteAbstract To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers’ demand for assistance in India. The presented study uses the data from the Kisan Call Centres, a nationwide network of farmer’s helplines, including 28.6 million call-log records, made available by the Ministry of Agriculture & Farmers’ Welfare, Government of India. Additionally, the extracted insights are presented in the form of “Topic-wise Problems’ Trend Clusters” (TPTC), which can be used by policymakers in both the government and private sectors to aid decision-making. The article also introduces a pipeline for designing forecasting models to estimate the monthly frequency of farmer inquiries (in terms of the number of query calls). The seven statistical forecasting models were examined in the study with the TBATP1 (Trigonometric seasonal components with Box-Cox transformation incorporating ARIMA errors and Trend including the Seasonal components) model attaining the lowest error rates in terms of Root Mean Square Error (0.034) and Mean Absolute Error (0.107). The study also explores numerous applications of the derived insights in the real world as well as the future scope of the presented work.https://doi.org/10.1038/s41598-024-80488-xAgricultural problems analyticsArtificial intelligence in agricultureForecastingHelpline centre dataMachine learning. |
| spellingShingle | Samarth Godara Shbana Begam Ram Swaroop Bana Jatin Bedi Rajni Jain Md. Ashraful Haque Rajender Parsad Sudeep Marwaha Madhu Patial Saber Shirzad Ravi Nirmal TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand Scientific Reports Agricultural problems analytics Artificial intelligence in agriculture Forecasting Helpline centre data Machine learning. |
| title | TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand |
| title_full | TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand |
| title_fullStr | TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand |
| title_full_unstemmed | TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand |
| title_short | TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand |
| title_sort | tptc topic wise problems trend clusters for smart agricultural insights extraction and forecasting of farmer s information demand |
| topic | Agricultural problems analytics Artificial intelligence in agriculture Forecasting Helpline centre data Machine learning. |
| url | https://doi.org/10.1038/s41598-024-80488-x |
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