# A Neuro-Fuzzy Approach to Selecting Crops in Vertical Irrigation

## Abstract

Uncontrolled use of land resources and an ever increasing population has led to a scarcity of land in many countries, especially in Asia where population is higher than in other parts of the world. Also, the recent growth in urban populations has induced the use of forest land for agriculture or for residential purposes. In some countries governments are encouraging people to opt for vertical residences (multistoried apartments) where a single area is used to accommodate more than one family. In countries like China and Japan, where land scarcity is acute, people practice agriculture in multistoried structures. But irrigation requirements for this kind of agricultural practice are different from those of conventional procedures. Not all crops can be cultivated inside apartments due to the controlled nature of the inside environment. Thus the present study will try to find a methodology for selecting suitable species of crop for indoor cultivation ensuring the desired level of yield under minimum uncertainty.

## Keywords

Vertical irrigation Fuzzy logic Neuro-genetic model## References

- Abdullayev NT, Ismaylova KS (2011) Use of neural networks for recognition of pathological changes in stimulative electromyograms. Biomed Eng 45(6):201–206CrossRefGoogle Scholar
- Akbari S, Hemingson HB, Beriault D, Simonson CJ, Besant RW (2012) Application of neural networks to predict the steady state performance of a run-around membrane energy exchanger. Int J Heat Mass Transf 55(5–6):1628–1641CrossRefGoogle Scholar
- Aqil M, Kita I, Yano A, Nishiyama S (2007) A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J Hydrol 337(1–2):22–34CrossRefGoogle Scholar
- Barkhatov NA, Revunov SE (2010) Neural network classification of discontinuities in space plasma parameters. Geomagn Aeron 50(7):894–904CrossRefGoogle Scholar
- Centre For Evidence Based Medicine (2012) SpPins and SnNouts. Retrieved from http://www.cebm.net/index.aspx?o=1042. 25 May 2012
- Doğan E, Yüksel İ, Kişi Ö (2007) Estimation of total sediment load concentration obtained by experimental study using artificial neural networks. Environ Fluid Mech 7(4):271–288. doi: 10.1007/s10652-007-9025-8 CrossRefGoogle Scholar
- Duin RPW (2000) Learned from neural network. Retrieved from http://homepage.tudelft.nl/a9p19/papers/. 25 May 2012
- GANFYD (2012) Statistical test for agreement. Retrieved from http://www.ganfyd.org/index.php?title=Statistical_tests_for_agreement. 25 May 2012
- Golmohammadi D (2011) Neural network application for fuzzy multi-criteria decision making problems. Int J Prod Econ 131(2):490–504CrossRefGoogle Scholar
- Gupta L, McAvoy M (2000) Investigating the prediction capabilities of the simple recurrent neural network on real temporal sequences. Pattern Recognit 33(12):2075–2081CrossRefGoogle Scholar
- Hussain MA (1999) Review of the applications of neural networks in chemical process control – simulation and online implementation. Artif Intell Eng 13(1):55–68CrossRefGoogle Scholar
- Jain BA, Nag BN (1998) A neural network model to predict long-run operating performance of new ventures. Ann Oper Res 78(0):83–110. doi: 10.1023/A:101891040273 CrossRefGoogle Scholar
- Janga Reddy M, Nagesh Kumar D (2006) Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour Manag 20(6):861–878CrossRefGoogle Scholar
- Kulkarni MA, Patil S, Rama GV, Sen PN (2008) Wind speed prediction using statistical regression and neural network. J Earth Syst Sci 117(4):457–463CrossRefGoogle Scholar
- Laurent J-M, François L, Bar-Hen A, Bel L, Cheddadi R (2008) European bioclimatic affinity groups: data-model comparisons. Global Planet Chang 61(1–2):28–40CrossRefGoogle Scholar
- Lebowitz M (1985) Categorizing numeric information for generalization. Cognit Sci 9(3):285–308CrossRefGoogle Scholar
- Liu Z, Peng C, Xiang W, Deng X, Tian D, Zhao M, Yu G (2012) Simulations of runoff and evapotranspiration in Chinese fir plantation ecosystems using artificial neural networks. Ecol Model 226:71–76Google Scholar
- Lohani AK, Kumar R, Singh RD (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442–443:23–35CrossRefGoogle Scholar
- Mafakheri E, Tahmasebi P, Ghanbari D (2012) Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition. Measurement 45(6):1387–1395CrossRefGoogle Scholar
- Midgley AR Jr, Niswender GD, Rebar RW (1969) Principles for the assessment of the reliability of radioimmunoassay methods (precision, accuracy, sensitivity, specificity). Eur J Endocrinol 142:163–184. doi: 10.1530/acta.0.062S163 CrossRefGoogle Scholar
- Morita S, Nemoto K (1995) Structure and function of roots. In: Proceedings of the fourth international symposium on structure and function of roots, Kluwer, Stará Lesná, Slovakia, pp 75–86, 20–26 June 1993Google Scholar
- Moustra M, Avraamides M, Christodoulou C (2011) Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst Appl 38(12):15032–15039CrossRefGoogle Scholar
- Park CH, Joo JG, Kim JH (2012) Integrated washland optimization model for flood mitigation using multi-objective genetic algorithm. J Hydro Environ Res 6(2):119–126CrossRefGoogle Scholar
- Pinthong P, Gupta AD, Babel MS, Weesakul S (2009) Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water Resour Manag 23(4):697–720CrossRefGoogle Scholar
- Tan EM, Smolen JS, McDougal JS, Butcher BT, Conn D, Dawkins R, Fritzler MJ, Gordon T, Hardin JA, Kalden JR, Lahita RG, Maini RN, Rothfield NF, Smeenk R, Takasaki Y, Van Venrooij WJ, Wiik A, Wilson M, Koziol JA (1999) A critical evaluation of enzyme immunoassays for detection of antinuclear autoantibodies of defined specificities: I. Precision, sensitivity, and specificity. Arthritis Rheum http://dx.doi.org/10.1002/1529-0131(199904)42:3<455::AID-ANR10>3.0.CO;2-3, doi: 10.1002/1529-0131(199904)42:3%3c455::AID-ANR10%3e3.0.CO;2-3
- Teegavarapu RSV (2007) Use of universal function approximation in variance-dependent surface interpolation method: an application in hydrology. J Hydrol 332(1–2):16–29CrossRefGoogle Scholar
- Tomandl D, Schober A (2001) A Modified General Regression Neural Network (MGRNN) with new, efficient training algorithms as a robust ‘black box’-tool for data analysis. Neural Netw 14(8):1023–1034CrossRefGoogle Scholar
- Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76(3–4):392–410CrossRefGoogle Scholar
- Waszczyszyn Z, Ziemiański L (2001) Neural networks in mechanics of structures and materials – new results and prospects of applications. Comput Struct 79(22–25):2261–2276CrossRefGoogle Scholar
- Wei C-C, Chen L, Hsu H-H (2012) Neural-based decision trees classification techniques: a case study in water resources management. Lecture notes in electrical engineering, 1. Recent Adv Comput Sci Info Eng 124:377–382Google Scholar
- Wu CL, Chau KW (2011) Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399(3–4):394–409CrossRefGoogle Scholar
- Xia L, Ruifeng Y, Jianfang J (2012) The control system of electric load simulator based on neural network. In: Advances in future computer and control systems. Advances in intelligent and soft computing, vol 159, pp 681–687Google Scholar
- Yamaguchi T, Mackin KJ, Nunohiro E, Park JG, Hara K et al (2008) Artificial neural network ensemble-based land-cover classifiers using MODIS data. Artif Life Robot 13(2):570–574CrossRefGoogle Scholar
- Zeng Z (2012) Advances in intelligent and soft computing. Advances in computer science and information engineering, vol 169, pp 431–436Google Scholar