ICTACT Journal on Soft Computing

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Novel Region Specific Decision Support System for Crop Selection and Cultivation


Affiliations
1 Department of Computer Science, Mother Teresa Women’s University, India
     

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The prime concern of any country is Agriculture. Every nation has to feed its population by making strong policy support for Agricultural production. This paper deals with providing decision support for the crop to be selected for cultivation based on several influencing parameters. Even though there are many decision support systems available for Agriculture, there is a lack in region specific ones. The proposed system aims to overcome the aforementioned issue. For this purpose, the system considers climatic data from the Government of India web portal, Tamil Nadu Agricultural University portal and reports. The precision data collected from the fields will be given as inputs to the proposed system. The crops to be selected for cultivation are based on the historical data and guidelines from the Tamil Nadu Agritech portal. The accuracy of the proposed decision support system is assessed by getting feedback from the farmers.

Keywords

Accuracy, Agriculture, Crop Selection, Decision Support System, Historical Data.
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  • R. Wanjari, K.G. Mandal, P. Ghosh, T. Adhikari and N.H. Rao, “Rice in India: Present Status and Strategies to Boost Its Production Through Hybrids”, Journal of Sustainable Agriculture, Vol. 28, No. 2, pp. 19-39, 2006.

  • K. Thiyagarajan and R. Kalaiyarasi, “Status Paper on Rice in Tamilnadu”, Rice Knowledge Management Portal Publisher, 2010.

  • R. Rupnik, M. Kukar, P. Vracar and Z. Bosnic, “AgroDSS: A Decision Support System for Agriculture and Farming”, Computers and Electronics in Agriculture, Vol. 161, pp. 260-271, 2019.

  • G. Yogeswari and A. Padmapriya, “Recommender System for Nutrient Management Based on Precision Agriculture”, International Journal of Recent Technology and Engineering, Vol. 8, No. 4, pp. 1-12, 2019.

  • Claudio Stockle, Marcello Donatelli and Roger Nelson, “CropSyst, A Cropping Systems Simulation Model”, European Journal of Agronomy, Vol. 18, No. 3-4, pp. 289-307, 2003.

  • G. Yogeswari and A. Padmapriya, “Precision Data Acquisition and Analysis for Nutrient Management of Tomatoes”, Asian Journal of Computer Science and Technology, Vol. 8, No. 2, pp. 20-23, 2019.

  • J. Lindblom and M. Ljung, “Next Generation Decision Support Systems for Farmers: Sustainable Agriculture through Sustainable IT”, Proceedings of 11th European Symposium on International Farming Systems Association, pp. 1-6, 2014.

  • R. Ruba Mangala and A. Padmapriya, “Visualizing the Impact of Climatic Changes on Pest and Disease Infestation in Rice”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 8413-8421, 2019.

  • M. Abedinpour, A. Sarangi, T.B.S. Rajput, M. Singh and T. Ahmad, “Performance Evaluation of Aqua Crop Model for Maize Crop in a Semi-Arid Environment”, Agricultural Water Management, Vol. 110, pp. 55-66, 2012.

  • R.R. Mangala and A. Padmapriya, “Prediction Based Agro Advisory System for Crop Protection”, Proceedings of International Conference on Intelligent Data Communication Technologies and Internet of Things, pp. 1-7, 2019.

  • Venkatalakshmi Balakrishnan, “Decision Support System for Precision Agriculture”, International Journal of Research in Engineering and Technology, Vol. 3, No. 19, pp. 849-852, 2014.

  • Guidelines for Rice, Available at: http://www.knowledgebank.irri.org/decision-tools/rice-doctor, Accessed on 2020.

  • Production and Irrigation Statistics, Available at: https://visualize.data.gov.in/all_visualization, Accessed on 2020.

  • Water Related Data, Available at: https://www.indiawaterportal.org/datafinder, Accessed on 2020.

  • Paddy Expert Systems, Available at: http://agritech.tnau.ac.in/expert_system/paddy/Index.html, Accessed on 2020.

  • Cropstaticsrice, Available at: https://www.farmer.gov.in/cropstaticsrice.aspx, Accessed on 2020.

  • Yaser Sakkaf, “Decision Trees: ID2 Algorithm Explained”, Available at: https://towardsdatascience.com/decision-trees-for-classification-id3-algorithm-explained-89df76e72df1, Accessed on 2020.


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  • Novel Region Specific Decision Support System for Crop Selection and Cultivation

Abstract Views: 297  |  PDF Views: 0

Authors

P. Brindha
Department of Computer Science, Mother Teresa Women’s University, India
K. Kavitha
Department of Computer Science, Mother Teresa Women’s University, India

Abstract


The prime concern of any country is Agriculture. Every nation has to feed its population by making strong policy support for Agricultural production. This paper deals with providing decision support for the crop to be selected for cultivation based on several influencing parameters. Even though there are many decision support systems available for Agriculture, there is a lack in region specific ones. The proposed system aims to overcome the aforementioned issue. For this purpose, the system considers climatic data from the Government of India web portal, Tamil Nadu Agricultural University portal and reports. The precision data collected from the fields will be given as inputs to the proposed system. The crops to be selected for cultivation are based on the historical data and guidelines from the Tamil Nadu Agritech portal. The accuracy of the proposed decision support system is assessed by getting feedback from the farmers.

Keywords


Accuracy, Agriculture, Crop Selection, Decision Support System, Historical Data.

References