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Proceedings Paper

Setting up an autonomous hyperspectral flying platform for precision agriculture (Conference Presentation)
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Paper Abstract

Precision agriculture is a farming management concept based on the use of advanced sensors for monitoring crops. Among the different alternatives to host these devices, Unmanned Aerial Vehicles (UAVs) arises as a good tradeoff between costs, spatial resolution and effective time needed during inspections, overcoming other difficulties presented in Earth Observation (EO) satellites or airborne remote sensing. In this work the authors present the decisions, mounting and problems encountered in the development of an UAV platform for precision agriculture since its conception at the early stages. This flying platform has a payload which comprises an industrial VIS/NIR hyperspectral camera, an RGB camera and a GPU. Due to the features encountered in hyperspectral sensors, hundreds of bands are able to be captured at a time, which means that it is possible to calculate several Vegetation Index (VI), in which two or more bands give information related to the vegetation properties such as vigor assessment, water status, biomass prediction and health monitoring just to name some. This study is being focused on an extensive vineyard in the island of Gran Canaria, Spain. However, the limitation of present LiPo batteries together with the inclusion of heavy payload in the UAVs impose severe restrictions in their autonomy, and hence in this work a software has been developed in order to optimize the trajectory of the drone based on the coordinates of the field to be inspected, the height of flight, speed and the percentage of battery left. This code is included in the GPU, which is also in charge of controlling the sensors and synchronize the images obtained by the RGB sensor with the lines obtained by the pushbroom hyperspectral sensor and the GPS coordinates. Preliminary images and results will be given from the first flights of this platform and also with the analysis made to some winery laves in our laboratories with our VIS/NIR/SWIR infraesturcture.

Paper Details

Date Published: 11 October 2018
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Proc. SPIE 10792, High-Performance Computing in Geoscience and Remote Sensing VIII, 1079206 (11 October 2018); doi: 10.1117/12.2503096
Show Author Affiliations
Aythami Salvador Rodriguez Valentin, Univ. de Las Palmas de Gran Canaria (Spain)
Pablo Horstrand, Univ. de Las Palmas de Gran Canaria (Spain)
José Fco. López , Univ. de Las Palmas de Gran Canaria (Spain)
Sebastián López, Univ. de Las Palmas de Gran Canaria (Spain)


Published in SPIE Proceedings Vol. 10792:
High-Performance Computing in Geoscience and Remote Sensing VIII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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