ICUAS 2020 Paper Abstract

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Paper ThC3.6

Karatzinis, Georgios (Democritus University of Thrace), Apostolidis, Savvas (Democritus University of Thrace), Kapoutsis, Athanasios (Democritus University of Thrace), Panagiotopoulou, Liza (GEOTOPOS S.A.), Boutalis, Yiannis (Democritus University of Thrace), Kosmatopoulos, Elias (Democritus University of Thrace and CERTH, Greece)

Towards an Integrated Low-Cost Agricultural Monitoring System with Unmanned Aircraft System

Scheduled for presentation during the Regular Session "UAS Applications III" (ThC3), Thursday, September 3, 2020, 18:40−19:00, Edessa

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords UAS Applications, Path Planning, Standardization

Abstract

Over the last years, an intensified interest has been shown in many studies for precision agriculture. Unmanned Aircraft Systems (UASs) are capable of solving a plethora of surveying tasks due to their flexibility, independence and customization. The incorporation of UASs remote sensing in precision agriculture enhances the abilities of crop mapping, management and identification through vegetation indices. In addition to this, different image analysis and computer vision processes were adopted trying to facilitate field operations in cooperation with human intervention to enhance the overall performance. In this paper, we present a practically oriented application on vineyards towards an integrated low-cost system which utilizes Spiral-STC (Spanning Tree Coverage) algorithm as a Coverage Path Planning (CPP) method. Based on the resulted flight campaign, UAV images were collected, and the incorporated image analysis processes finally extract vegetation knowledge. Also, geo referenced orthophotos and computer vision applications complete the generated oversight of the field. These supportive tools provide farmers with useful information (crop health indicators, weather predictions) letting them extrapolate knowledge and identify crop irregularities.

 

 

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