@conference{Livanos2020,

title = {Extraction of Reflectance Maps for Smart Farming Applications Using Unmanned Aerial Vehicles},

author = { G. Livanos and D. Ramnalis and V. Polychronos and P. Balomenou and P. Sarigiannidis and G. Kakamoukas and T. Karamitsou and P. Angelidis and M. Zervakis},

editor = { Networks 2020 12th International Symposium on Communication Systems and Digital Signal Processing ({CSNDSP})},

url = {https://www.researchgate.net/publication/343306275_Extraction_of_Reflectance_Maps_for_Smart_Farming_Applications_Using_Unmanned_Aerial_Vehicles},

doi = {10.1109/csndsp49049.2020.9249628},

year  = {2020},

date = {2020-07-01},

booktitle = {2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)},

journal = {2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020},

publisher = {IEEE},

abstract = {In this application paper, a robust framework for smart remote sensing of cultivations using Unmanned Aerial Vehicles is presented, yielding to a useful tool with advanced capabilities in terms of time-efficiency, accuracy, user-friendly operability, adjustability and expandability. The proposed system incorporates multispectral imaging, automated navigation and real-time monitoring functionalities into a fixed-wing Unmanned Aerial Vehicle platform. Offline analysis of captured data is performed, at this stage of system development, via powerful commercial software so as to extract the reflection map of the crop area under study based on the Normalized Difference Vegetation Index. The proposed approach has been tested on selected cultivations in two regions (Greece), aiming at recording field variability and early detecting factors related to crop stress. Preliminary results indicate that the proposed framework can prove a cost-effective, precise, flexible and operative solution for agriculture industry, enabling the application of smart farming procedures for productive farm management. Adopting a collaborative group of aerial vehicles via Flying Ad hoc Networks, the proposed sensing approach could be further enhanced for large-scale applications, fusing data from multiple nodes into an advanced Decision Support System and providing information on bigger areas at the same time with respect to a single sensing source. © 2020 IEEE.},

keywords = {flying ad-hoc networks, multispectral imaging, reflectance map, remote sensing, smart farming, spectral signature, unmanned aerial vehicles, vegetation index},

pubstate = {published},

tppubtype = {conference}

}