Decision-making tool for crop selection for agriculture development
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In the present competitive environment, a farmer needs better education, business expertise and good knowledge of technologies and tools to be successful in agriculture. Farmers usually select crop for cultivation according to their traditional knowledge and past experience in farming, but a farmer’s predictions may go wrong due to natural disaster. Thus, decision-making tool need to be developed to help farmers to take decision on crop cultivation. In this paper, decision-making tool was developed for selecting the suitable crop that can be cultivated in a given agricultural land. In the present study, 26 input variables were identified and categorized into six broad heads of main variables such as soil, water, season, input, support and infrastructure. Each main variable has several sub-variables. The priority weights for the variables were determined using the dominance-based rough set approach. In order to convert sub-variable sequences to main variable sequences, evaluation scores of each main variable were calculated by applying the weights of sub-variables and by using simple additive method. Finally, the evaluation scores were applied to Johnson’s reduct algorithm and classification rules were generated. The developed tool predicts each site in the datasets into one of the three crops such as paddy, groundnut and sugarcane. In order to validate the performance of the tool, the same datasets were predicted again by agriculture experts. The results obtained from the tool showed 92% agreement with the results obtained from the experts. Thus, the tool is a feasible tool for cultivating the suitable crops in the agricultural sites.
KeywordsAgriculture Classification Dominance-based rough set approach Johnson’s reduct Crop selection
This work forms part of the R&D activities of TIFAC-CORE in Automotive Infotronics located at VIT University, Vellore. The authors would like to thank DST, Government of India, for providing necessary hardware and software support for completing this work successfully.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 5.Rong Z, Sifeng L, Bin L (2009) A method for weight assignment by dominance-based rough sets approach. In: 2009 Chinese control and decision conference. IEEE, pp 6060–6065Google Scholar
- 13.Wei HL, Billings SA (2007) Feature subset selection and ranking for data dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):162–166.Google Scholar
- 14.Bazan JG, Nguyen HS, Nguyen SH, Synak P, Wróblewski J (2000) Rough set algorithms in classification problem. In: Polkowski L, Tsumoto S, Lin TY (eds) Rough set methods and applications). Physica-Verlag, Heidelberg, pp 49–88Google Scholar
- 15.Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Slowinski R (ed) Intelligent decision support. Handbook of applications and advances of the rough sets theory. Kluwer, Dordrecht, pp 331–362Google Scholar
- 16.Bazan JG, Nguyen HS, Nguyen SH, Synak P, Wróblewski J (2000) Rough set algorithms in classification problem. In: Rough set methods and applications (studies in fuzziness and soft computing), vol 56. Springer, Heidelberg, pp 49–88Google Scholar
- 17.Chatterjee R, Guha D, Sanyal DK, Mohanty SN (2016) Discernibility matrix based dimensionality reduction for EEG signal. In: Hand, 140, p 140Google Scholar
- 18.Akbar Z (2003) Marketing data classification using Johnson’s algorithm. In: Knowledge discovery and discrete mathematics. Springer, pp 257–266Google Scholar
- 19.Gholap J, Ingole A, Gohil J, Gargade S, Attar V (2012). Soil data analysis using classification techniques and soil attribute prediction. arXiv preprint arXiv:1206.1557
- 21.Mustafa AA, Singh M, Sahoo RN, Ahmed N, Khanna M, Sarangi A, Mishra AK (2011) Land suitability analysis for different crops: a multi criteria decision making approach using remote sensing and GIS. Researcher 3(12):1–24Google Scholar
- 23.AliKhashashneh EA, Al-Radaideh QA (2013) Evaluation of discernibility matrix based reduct computation techniques. In: 2013 5th international conference on computer science and information technology (CSIT). IEEE, pp. 76–81Google Scholar
- 24.Deepa N, Ganesan K (2016) Multi-class classification using hybrid soft decision model for agriculture crop selection. Neural Comput Appl 1–14Google Scholar