Abstract
Irrigation in agricultural lands plays a crucial role in water and soil conservation. Real-time prediction of soil moisture content using wireless sensor network (WSN) based soil and environmental parameters sensing may provide an efficient platform to meet the irrigation requirement of agriculture land. In this research article, we have proposed Resilient Back-propagation optimization technique to train neural network pattern classification algorithm for the prediction of soil moisture content. Finally, the predicted soil moisture content is used by fuzzy weather model for generating adequate suggestions regarding irrigation requirement. The fuzzy model is developed by considering different weather parameters like sun light intensity, wind speed, environment humidity and environment temperature. Different weather conditions like cloudy situation, low pressure, cyclone and storm conditions are simulated in the fuzzy model. The soil moisture content prediction algorithm is tested with soil moisture content in each 1 h advance by considering eleven different soil and environmental parameters collected during a field test. The prediction errors are analysed using MSE (Mean Square Error), RMSE (Root Mean Square Error), and R-squared error.
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References
Worldometers about world statistics updates in real time (2011).: Water consumed this year, http://www.worldometers.info/water/
Zhang, M., Li, M., Wang, W., Liu, C., Gao, H.: Temporal and spatial variability of soil moisture based on WSN. Math. Comput. Model. 58(3–4), 826–833 (2013)
Liang, R., Ding, Y., Zhang, X., Zhang, W.: A real time prediction system of soil mc using genetic neural-network based on annealing algorithm. In: IEEE International Conference on Automation and Logistics (ICAL-2008), pp. 2781–2785, 1–3 Sept 2008 (Computer & Inf. Eng. College, Hohai University, Changzhou)
Goumopoulos, C., O’Flynn, B., Kameas, A.: Automated zone-specific irrigation with wireless sensor/actuator network and adaptable decision support. Comput. Electron. Agric. 105, 20–33 (2014) (Computer Technology Institute, DAISy Research Unit, 26500 Rio Patras, Greece, Information & Communication Systems Engineering Department, Aegean University, Greece, Tyndall National Institute, Lee Maltings, Prospect Row, Cork, Ireland)
Chai, S.-S., Veenendaal, B., West, G., Walker, J.P.: Back-propagation neural network for soil moisture retrieval using nafe’05 data: a comparison of different training algorithms. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, Part B4. Beijing (2008)
Phillips, A.J., Newlands, N.K., Liang, S.H.L., Ellert, Benjamin H.: Integrated sensing of soil moisture at the field-scale: measuring, modeling and sharing for improved agricultural decision support. Comput. Electron. Agric. 104, 73–88 (2014)
Rehman, M.Z., Nawi, N.M.: The effect of adaptive momentum in improving the accuracy of gradient-descent back-propagation algorithm on classification problems. In: Software Engineering and Computer Systems Communications in Computer and Information Science, vol. 179, pp. 380–390. Springer, Berlin (2011)
Andrei, N.: Scaled conjugate gradient algorithms for unconstrained optimization. Comput. Optimization Appl. 38(3), 401–416 (2007)
Chel, H., Majumder, A., Nandi, D.: Scaled conjugate gradient algorithm in neural network based approach for handwritten text recognition. In: Trends in Computer Science, Engineering and Information Technology Communications in Computer and Information Science, vol. 204, pp. 196–210. Springer, Berlin (2011)
Riedmiller, M., Braun, H.: A direct adaptive method for faster back-propagation learning, The RPROP algorithm. In: IEEE International Conference on Neural Networks, vol. 1, pp. 586–591, 28 Mar 1993–01 Apr 1993 (Institut fur Logik, Komplexitat und Deduktionssyteme, University of Karlsruhe)
Dai, Y.-H.: A perfect example for the BFGS method. Math. Program. 138(1–2), 501–530 (2013)
Nocedal, J., Yuan, Y.-X.: Analysis of a self-scaling quasi-Newton method. Math. Program. 61(1–3), 19–37 (1993)
Cetişli, B., Barkana, A.: Speeding up the scaled-conjugate-gradient algorithm and its application in neural network fuzzy logic classifier training. Soft Comput. 14(4), 365–378 (2010)
Acknowledgements
The authors thank to All India Council for Technical Education (AICTE), New Delhi, India to provide support under Career Award for Young Teachers (CAYT) scheme 2013–2014 and help for continuing research work in the area of precision agriculture mechanism.
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Lenka, S.K., Mohapatra, A.G. (2016). Hybrid Decision Model for Weather Dependent Farm Irrigation Using Resilient Backpropagation Based Neural Network Pattern Classification and Fuzzy Logic. In: Satapathy, S., Das, S. (eds) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Smart Innovation, Systems and Technologies, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-30933-0_30
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DOI: https://doi.org/10.1007/978-3-319-30933-0_30
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