CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

A Fuzzy Logic based Agricultural Decision Support System for Assessment of Crop Yield Potential using Shallow Ground Water Table

by Mohammad Rafiuzzaman, Ibrahim Çil
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 9
Year of Publication: 2016
Authors: Mohammad Rafiuzzaman, Ibrahim Çil

Mohammad Rafiuzzaman, Ibrahim Çil . A Fuzzy Logic based Agricultural Decision Support System for Assessment of Crop Yield Potential using Shallow Ground Water Table. International Journal of Computer Applications. 149, 9 ( Sep 2016), 20-31. DOI=10.5120/ijca2016911526

@article{ 10.5120/ijca2016911526,
author = { Mohammad Rafiuzzaman, Ibrahim Çil },
title = { A Fuzzy Logic based Agricultural Decision Support System for Assessment of Crop Yield Potential using Shallow Ground Water Table },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 9 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 20-31 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016911526 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:54:17.341584+05:30
%A Mohammad Rafiuzzaman
%A Ibrahim Çil
%T A Fuzzy Logic based Agricultural Decision Support System for Assessment of Crop Yield Potential using Shallow Ground Water Table
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 9
%P 20-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Agricultural research is aimed towards increasing the productivity and food quality at reduced expenditure and with increased profit. One of the main challenges of this approach is to equip farmers with adequate and affordable information and control technology; as for higher crop yields, they need advanced expert knowledge to take proper decisions during land preparation, sowing, fertilizer management, irrigation management, integrated pest management, storage etc. In an effort to provide a methodology for better assessment on the functional outcome of this research area, an online fuzzy logic based agricultural decision support system is developed and presented in this paper. The aim of this system is to assist farmers in taking proper decisions for having a better crop production with less cost, despite the adverse nature of the soil on their farming area. Our proposed system focuses on utilizing abundant surface ground water available at the end of the wet season while benefiting from timely access to shallow groundwater from the process of capillary rises so that the farmers can have a better crop yield with or even without the expensive irrigations. The experiment was carried out in the northern and southern (coastal areas) regions of Bangladesh. Fuzzy logic is used in this case to handle uncertain or ambiguous data and knowledge of the ınput data. Experimental results presented in this paper also show that despite diverse climate nature, farmers can produce a hefty amount dry season crops in the coastal areas by utilizing shallow ground water, which was thought as impossible before. Though the experiment is carried out in Bangladesh only, if successfully implemented, this finding is believed to bring a groundbreaking agricultural advancements for the coastal area farmers in all over the world. Especially in the coastal areas of India, Myanmar, Nepal, Indonesia and Vietnam as their nature of the soil is almost same as Bangladesh.

  1. "CIA - The World Factbook". Central Intelligence Agency. Archived from the original on 29 June 2011. Last Updated: Jun 05, 2014.
  2. Population Reference Bureau. 2015 World Population Data Sheet. Washington, D.C. Accessed at: population-data-sheet_eng.pdf
  3. Johansson-Stenman, O., et al., Trust, trust games and stated trust: Evidence from rural Bangladesh. J. Econ. Behav. Organ. (2011), doi:10.1016/j.jebo.2011.06.022
  4. S.S. Hussain, H. Higgins, U.S. Embassy. USDA grain report: BG9003. Bangladesh Grain and Feed Annual 2009.
  5. Poulton, P.L., Saifuzzaman, M. 2010. Assessing potential additions to crop yields from shallow water tables for smallholder farmers during the Rabi season in southern Bangladesh. Proceedings of the 11th European Society of Agronomy Congress, August 28 – September 2010, Montpellier, France.
  6. Harvey, C.F., Ashfaque, K.N., Yu, W., Badruzzaman, A.B.M.,et al., 2006. Groundwater dynamics and arsenic contamination in Bangladesh, Chemical Geology, Volume 228, Issues 1-3, Controls on Arsenic Transport in NearSurface Aquatic Systems, 16 April 2006, Pages 112-136, ISSN 0009-2541
  7. Saifuzzaman, M., et al. "project Expanding the area for Rabi-season cropping in southern Bangladesh." (2012).
  8. K. Adhikari, B. Chakraborty, A. Gangopadhyay. Assessment of Irrigation Potential of Ground Water Using Water Quality Index Tool. Asian Journal of Water, Environment and Pollution. IOS Press. 2013
  9. Robins, N.S., Fergusson, J., 2014. Groundwater scarcity and conflict e managing hotspots. Earth Perspect. 1, 6.
  10. Landschoot, S., Waegeman, W., Audenaert, K., Van Damme, P., Vandepitte, J., De Baets, B., and Haesaert, G. 2013. A field-specific web tool for the prediction of Fusarium head blight and deoxynivalenol content in Belgium. Comput. Electron. Agric. 93:140-148.
  11. Sonal Dubey, R.K. Pandey, S.S. Gautam. Literature Review on Fuzzy Expert System in Agriculture. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013
  12. Rajeshwar G Joshi, Parag Bhalchandra, Dr.S.D.Khmaitkar. Predicting Suitability of Crop by Developing Fuzzy Decision Support System. International Journal of Emerging Technology and Advanced Engineering. ISSN 2250-2459 (Online), An ISO 9001:2008 Certified Journal,Volume 3, Special Issue 2, January 2013
  13. Anna Perini and Angelo Susu, “Developing a Decision Support System for Integrated Production in Agriculture”, Preprint submitted to Environmental Modelling and Software on 10 January 2003.
  14. DJ Power, R Sharda, F Burstein. Decision Support Systems Volume 7. Management Information Systems Published Online: 21 JAN 2015 DOI: 10.1002/9781118785317.weom070211
  15. Nam Nguyen, Malcolm Wegener, Iean Russell, “Decision support systems in Australian agriculture: state of the art and future development”, Contributed paper prepared for presentation at the International Association of Agricultural Economists Conference, Gold Coast, Australia, August 12-18, 2006
  16. PP Mumba, E Kambwiri. Water Quality of Irrigation Water into and out of an Irrigated Sugar Cane Plantation. Asian Journal of Water, Environment and Pollution. IOS Press. 2013
  17. E Ostrom, WF Lam, M Lee. The Performance of Self-Governing Irrigation Systems in Nepal. Human Systems Management, IOS Press, 1994
  18. Mirschel W, Wenkel K-O, Berg M, Wieland R, Nendel C, Köstner B, Topazh AG, Terleev VV, Badenko VL (2016) A spatial model-based decision support system for evaluating agricultural landscapes under the aspect of climate change. In: L. Mueller et al. (eds) Novel methods for monitoring and managing land and water resources in Siberia. Springer, Cham, pp 519–540 (Chapter 23 of this book)
  19. S. Madhan Babu, S. Pradeep, A. Shyamala, Ashutosh Das. Asian Journal of Water, Environment and pollution. IOS Press. 2007
  20. Stephen R. Heller, Stephen L. Rawlins. Agriculture systems research – A new initiative. Human Systems Management, 1986 - IOS Press. Pages: 289-296. June 14, 2013
  21. Parker, C.G. (1999), A user-centred design method for agricultural DSS. In U. Rickert (ed.) EFITA-99: Proceedings of the Second European Conference for Information Technology in Agriculture. Bonn, Germany. 27–30`h September 1999, Bonn: Universität Bonn-ILB. Vol A. pp. 395–404.
  22. Bryan Hosack, Dianne Hal, David Paradice, James F. Courtney. A Look Toward the Future: Decision Support Systems Research is Alive and Well. Journal of the Association for Information Systems Vol. 13, Issue 5, pp. 315-340, May 2012
  23. Edward C. Martin. Determining the Amount of Irrigation Water Applied to a Field. The University of Arizona College of Agriculture and life Sciences, Tucson, Arizona. 2006
  24. Verburg, K., 1996. Methodology in soil water and solute balance modelling: an evaluation of the APSIM-SoilWat and SWIMv2 models. CSIRO Division of Soils Technical Report No 131
  25. R. Bosma, J. Verreth, U. Kaymak, J. van den Berg, H. Udo. Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam. Soft Comput (2011) 15:295–310. DOI 10.1007/s00500-010-0618-7. Published online: 17 June 2010
  26. S. Madhan Babu, S. Pradeep, A. Shyamala, Ashutosh Das. Asian Journal of Water, Environment and pollution. IOS Press. 2007.
  27. Abu Sayed, Md. "Impact of lined canals on shallow tubewell irrigation and their acceptability by the farmers." (2010).
  28. Geebelen, Kristof, et al. "A MVC Framework for policy-based adaptation of workflow processes: a case study on confidentiality." Web Services (ICWS), 2010 IEEE International Conference on. IEEE, 2010.
  29. Khanale P.B.,Ambilwade R.P.,2011,‟A fuzzy Inference System for Diagnosis of Hyperthyrodism‟, Journal of Artificial Intelligence
  30. FAO. "The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)—Managing Systems at Risk." (2011): 288.
  31. Renard, D., et al. "Ecological engineers ahead of their time: The functioning of pre-Columbian raised-field agriculture and its potential contributions to sustainability today." Ecological Engineering 45 (2012): 30-44.
Index Terms

Computer Science
Information Sciences


Agriculture online Decision Support System fuzzy Logic flexible querying shallow groundwater Bangladesh