Abstract
The global food demand is increasing with the increase in world population. The agriculture land and fresh water resources are limited and the water crisis is further enhanced due to the global warming and the shortfall of better water management systems. The precision agriculture can play a very important role in curtailing this crisis by improving irrigation management techniques. These techniques can efficiently utilize the natural rainwater and ground water. It is also beneficial for the energy saving and achieving the better growth of crop. Further, to maintain the proper growth with optimal pesticide, the soil moisture of crop is needed to be maintained during its whole life cycle. Moisture of soil is an essential aspect for hydrology system that represents the typical circumstances in a limited volume of soil. An effective prediction of soil moisture can save the water and energy and it is essential to develop effective irrigation management system for this purpose. Prediction of soil moisture is vital for better irrigation management system. This paper describes result of experimental scenario for different machine learning regression techniques to predict the soil moisture.
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References
Gutiérrez, J., Villa-medina, J.F., Nieto-Garibay, A., et al.: Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans. Instrum. Meas. 63, 166–176 (2014). https://doi.org/10.1109/TIM.2013.2276487
Phillips, A.J., Newlands, N.K., Liang, S.H.L., Ellert, B.H.: Integrated sensing of soil moisture at the field-scale: measuring, modeling and sharing for improved agricultural decision support. Comput. Electron. Agric. 107, 73–88 (2014). https://doi.org/10.1016/j.compag.2014.02.011
Government of India NITI Aayog: Raising agricultural productivity and making farming remunerative for farmers (2015). http://niti.gov.in/content/working_papers.php. Accessed 13 March 2016
Vellidis, G., Tucker, M., Perry, C., et al.: A real-time wireless smart sensor array for scheduling irrigation. Comput. Electron. Agric. 61, 44–50 (2008). https://doi.org/10.1016/j.compag.2007.05.009
Adjei, S., Yankson, P.P.W.K., Principal, R.D., et al: Environment and globalizatio. Ph. D Proposal, pp. 1:1–1:40. (2012)https://doi.org/10.1017/cbo9781107415324.004
Shivling, V.D., Goap, A., Ghanshyam, C., et al.: A real time computational and statistical model (with high availability) of early warning for plant protection and pest control for crops (exp. kutki). In: 2015 IEEE International Conference on Computer Graphics, Vision and Information Security, CGVIS 2015, pp. 22–26 (2016)
Introduction to multiple regression. http://www.biddle.com/documents/bcg_comp_chapter4.pdf. Accessed 1 Feb 2018
Ridge regression. In: NCSS, LLC. https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Ridge_Regression.pdf. Accessed 1 Feb 2018
Christensen, R.: Analysis of Variance, Design, and Regression: Applied Statistical Methods, 1st edn, pp. 451–452. CRC Press, Boca Raton (2006)
Sagar, C.: The advantage of support vector regression (SVR) over simple linear regression (SLR) models for predicting real values (2017). https://www.kdnuggets.com/2017/03/building-regression-models-support-vector-regression.html. Accessed 31 January 2018
Goddard, N., Entekhabi, D.: Assessing the relationship between surface temperature and soil moisture in southern Africa. In: 2000 Remote Sensing and Hydrology, Proceedings of a Symposium Held at Santa Fe, 2000, pp. 296–301 (2001)
Forkuor, G., Hounkpatin, O.K.L., Welp, G., Thiel, M.: High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLoS ONE 12(1), e0170478 (2017)
Were, K., Bui, D.T., Dick, Ø.B., Singh, B.R.: A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol. Ind. 52, 394–403 (2015)
Lahmar, R., Bationo, B.A., Lamso, N.D., Guéro, Y., Tittonell, P.: Tailoring conservation agriculture technologies to West Africa semi-arid zones: building on traditional local practices for soil restoration. Field Crops Res. 132, 158–167 (2012)
Niang, I., et al.: Africa. In: Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., et al. (Eds.) Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change, pp. 1199–1265. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA (2014)
Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., et al.: Mapping soil properties of Africa at 250 m resolution: Random Forests significantly improve current predictions. PLoS ONE 10(6), 1–26 (2015)
Fabiyi, O.O., Ige-Olumide, O., Fabiyi, A.O.: Spatial analysis of soil fertility estimates and NDVI in South-Western Nigeria: a new paradigm for routine soil fertility mapping. Res. J. Agric. Environ. Manag. 2(12), 403–411 (2013)
Stevens, A., Miralles, I., van Wesemael, B.: Soil organic carbon predictions by airborne imaging spectroscopy: comparing cross-validation and validation. Soil Sci. Soc. Am. J. 76(6), 2174–2183 (2012)
Fujisada, H., Bailey, G.B., Kelly, G.G., Hara, S., Abrams, M.J.: Aster dem performance. IEEE Trans. Geosci. Remote Sens. 43(12), 2707–2714 (2005)
Ray, S., Singh, J., Das, G., Panigraphy, S.: Use of high resolution remote sensing data for generating site specific soil management plan. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 35, 127–132 (2004)
Acknowledgements
Authors sincerely express thanks to The Director, CSIR-CSIO, and Chandigarh, India for support to this research work at CSIR-CSIO. Furthermore, authors acknowledge Sh. Suman Tewary for providing valuable suggestions.
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Goap, A., Sharma, D., Shukla, A.K., Krishna, C.R. (2018). Comparative Study of Regression Models Towards Performance Estimation in Soil Moisture Prediction. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_31
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