LAUSR

Abstract Farmers, agencies, agricultural research community and firms require access to tools to analyze and estimate stressed and productive regions to obtain higher yield. At present, this is performed manually using visual interpretation. Recently there has been some development in the detection and mapping of the stressed crop by use of hyperspectral analysis; but, there is an information gap between farmers and information about the location of the crop under stress in the given area. There is an urgent need to provide a robust solution to identify the stressed region in the agricultural area. To address this, a unique application called as VegNet (Vegetative-Network) has been developed, which aims to provide the necessary tools to detect stressed crop locations using the spectral images obtained from UAVs, and provide stressed crops condition, location and area covered by those stressed crops. In this paper, a combination of spectral vegetation indices techniques has been highlighted to produce a comprehensive solution for precision agriculture using a UAV and VegNet. This incorporates several algorithms; segmentation, Canny-edge detector, dilation, gap-filling, image extraction and locating the stressed region using spectral modelling based Graphical User Interface (GUI) application for precision agriculture, societal benefit and Environmental research.