• Pankti Gorji
• Prof. Chintan Thacker
• Dr. Markand Oza
• Mr. Ujjwal Gupta

Agriculture resources are among the important renewable dynamic natural resources. Comprehensive, reliable and timely information on agricultural resources is very much necessary for a country like India whose mainstay of the economy is agriculture. Efficient crop management practices require accurate timely and rapid information about crop distributions. Since agricultural crops are dynamic, it is often useful to observe their development over time. Therefore, multi-temporal data was used for crop discrimination. In present study, data from LISS-3 sensor of Indian Remote Sensing (IRS) satellite was acquired during Rabi season (from early December to late January) of 2004 to 2017. The LISS-3 data of 24m resolution, as well as with Green, Red, NIR and SWIR band is used to derive NDVI (Normalized Difference Vegetation Index) images. Total 41 cloud free set of data were taken and stacked together to form a time series data. Hybrid approach is used for the classification of multi-date NDVI data set. Association rules are used for crop prediction. This paper demonstrates results of developed non-parametric methodology for identifying crop sowing pattern over Madhya Pradesh region.

System Identification, Association Rules, LISS-3, Multi temporal Series, NDVI.

"NONPARAMETRIC CROP SYSTEM IDENTIFICATION USING LISS-3 SATELLITE DATA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.648-651, November-2018, Available :http://www.jetir.org/papers/JETIR1811493.pdf

"NONPARAMETRIC CROP SYSTEM IDENTIFICATION USING LISS-3 SATELLITE DATA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp648-651, November-2018, Available at : http://www.jetir.org/papers/JETIR1811493.pdf