Abstract
With advancement of spatial and machine learning techniques, remote sensing dataset is rapidly being used in agriculture domain. In this paper, districtwise time-series precipitation data and multi-date normalized difference vegetation index (NDVI) results of Rajasthan state coupled with regression techniques of machine learning to evaluate districtwise regression models and make out patterns of vegetation status. The K-fold cross-validations have been used to measure the strength of the best regression model for entire Rajasthan state and districtwise. The results conclude that support vector machine regression model outperforms with 0.80 correlation, 0.800 RSquare, 0.040 RMSE, and 96.610% accuracy. Decision tree performs outstanding model for agriculture status in most of the districts of Rajasthan state for low--medium range vegetation state.
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References
H. Goyal, C. Sharma, N. Joshi, An integrated approach of GIS and spatial data Mining in big Data. Int. J. Comput. Appl. 169(11), 1–6 (2017)
P.S. Thenkabail, J.G. Lyon, Hyperspectral Remote Sensing of Vegetation (CRC Press, 2016)
H. Goyal, C. Sharma, N. Joshi, Spatial data mining: an effective approach in agriculture sector. Int. J. Sci. Res. Rev. 6(4) (2017)
H. Goyal, N. Joshi, C. Sharma, An empirical analysis of geospatial classification for agriculture monitoring. Proc. Comput. Sci. 132, 1102–1112 (2018)
H. Goyal, C. Sharma, N. Joshi, An effective geospatial association rule approach in agriculture monitoring, in IEEE International Conference on Computing for Sustainable Global Development (BVICAM-INDIACOM 2018) (In print)
H. Goyal, N. Joshi, C. Sharma, Feature extraction in geospatio-temporal satellite data for vegetation monitoring, in Emerging Trends in Expert Applications and Security (Springer, Singapore, 2019), pp. 177–187
H. Goyal, C. Sharma, N. Joshi, Estimation of monthly rainfall using machine learning approaches, International Conference on Innovations in Control, Communication and Information Systems (ICICCI), IEEExplore Digital Library https://doi.org/10.1109/iciccis.2017.8660837, March 2019
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten, Using model trees for classification. Mach. Learn. 32(1), 63–76 (1998)
A. Liaw, M. Wiener, Classification and regression by random forest. R News 2(3), 18–22 (2002)
M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: The RPROP algorithm, in IEEE International Conference on Neural Nets, pp. 586–591 (1993)
E.M. Abdel-Rahman, F.B. Ahmed, R. Ismail, Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int. J. Remote. Sens. 34(2), 712–728 (2013).
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Goyal, H., Singhal, S., Sharma, C., Punia, M. (2021). An Empirical Analysis of Spatial Regression for Vegetation Monitoring. In: Bhatia, S.K., Tiwari, S., Ruidan, S., Trivedi, M.C., Mishra, K.K. (eds) Advances in Computer, Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1158. Springer, Singapore. https://doi.org/10.1007/978-981-15-4409-5_64
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DOI: https://doi.org/10.1007/978-981-15-4409-5_64
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