Computational Modeling and Analysis of Precision Agriculture

Abstract

newline The agricultural activity dynamics is the notion of how the agricultural activities are newlineevolving in time and space across a particular geographical context. The agricultural yield newlinedynamics is characterized in both temporal and spatial contexts. The Climatic change impacts newlinethe agricultural yield directly and indirectly in both modes. The temperature and precipitation newlinehave been the key factors over the years in the historical perspective. The remote sensing newlineprocess provides both temporal and spatial data about role playing parameters like climatic newlinevariables, soil parameters and other contextual parameters like vegetation indices through the newlinevery near or real-time and archival satellite data of these parameters. Satellite datasets driven newlineyield estimation procedures pave the way to develop befitting and robust techniques to map newlineand predict regional as well as national-scale spatial crop yield. In a nutshell, due to their newlineworldwide coverage, high temporal resolution and long-term data repositories, MODIS and newlineAVHRR databases have been widely utilized on various scales in crop monitoring as well as newlineyield mapping and prediction. Satellite-derived metrics such as NDVI, EVI, and SAVI, among newlineothers, are frequently used for crop growth monitoring and yield prediction. newline newlineThe vegetation trends and dynamics are very crude indicators of yield dynamics whereas newlinethe simulated yield for a given period can provide the near-real picture of how the yield newlinechanges over time and space. So historical yield estimation is required to get the yield trends newlineacross time and space. The agricultural yield estimation at the regional scale is generally newlinemodeled using empirical computational models. The yield dynamics is more often newlinecharacterized by the identified trends. This can be understood through monotonic increasing newlineand decreasing trends such as non-parametric tests. The majority of the current and past newlineresearch focuses on predicting agricultural yields during the early growing season utilizing newlinedifferent sate