OPTIMIZING SENSOR MODULE SPECIFICATIONS FOR CROP MONITORING USING MULTI-SPECTRAL UAV MAPPING

Abstract

The sensor networks paradigm has revolutionized agricultural methods and has helped explicate the precincts of the agricuhural sector. The advancement of Wireless Sensor Networks and its integration with RS has spurred a new direction.of agricuhural mechanisms. Optimizing sensor module specification and in-situ sensor placement for crop monitoring and withal using an Unmanned Aerial Vehicle (UAV) platform for achieving the aforemention ed objective is a territory of precision agriculture yet to be explored. Images taken by low ahitude remote sensing platforms, or UAVs are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. The extensiveness of agricultural operations demand cost effectiveness in real- time data acquisition methods proposed for precision agriculture. Hence, it is essential that placement of these sensor modules is optimized for effective monitoring in terms of cost and operations. Optimized sensor module specification and placement involves explicit study and analysis of the field area using UAV imagery, sensor module design and development of a system that displays the data in real time on a user friendly web application. Spot irrigation control decisions means less water stress, improved crop quality, and significantly higher growth rates leading to reduced production time and eliminating pre-existing practices of over irrigating land. By cutting back on irrigation and quantifying the use of agricultural resources including energy, water, fertilizer and machinery at each stage of the agricultural process, conservation can be achieved which impacts your bottom line and subsequently the environment. Along with less utilization of resources that leads to monetary benefits, the project also involves a web application using which the user will be able to monitor the crops in real time from anywhere.