High precision agriculture : an application of improved machine-learning algorithms

Treboux, Jérôme (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

This paper presents the performances of machine learning algorithms on aerial images object detection for high precision agriculture. The dataset used focuses on geotagged pictures of vineyards. We demonstrate that advanced machine learning methodologies like Decision Tree Ensemble, outperform state-of-the-art image recognition algorithms generally used within the agriculture field. The innovative approach described here improve object detection and obtain an accuracy of 94.27% which is an increase of more than 4% compared to the state-of-the-art. Finally, methodology and possible developments for high precision agriculture is discussed in this study.


Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Bern, Switzerland, 14 June 2019
Date:
2019-06
Bern, Switzerland
14 June 2019
Pagination:
6 p.
Published in:
Proceedings of the 6th Swiss Conference on Data Science (SDS) 2019
DOI:
ISBN:
978-1-7281-3105-4
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-10-22, last modified 2019-10-22

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