Improved machine learning methodology for high precision agriculture

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 impact of machine learning in precision agriculture. State-of-the art image recognition is applied on a dataset composed of high precision aerial pictures of vineyards. The study presents a comparison of an innovative machine learning methodology compared to a baseline used classically on vineyard and agricultural objects. The baseline uses color analysis and is able discriminates interesting objects with an accuracy of 89.6 %. The machine learning innovative approach demonstrates that the results can be improved to obtain 94.27 % of accuracy. Machine Learning used to enrich and improve the detection of precise agricultural objects is also discussed in this study and opens new perspectives for the future of high precision agriculture.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Bilbao, Spain, 04-07 June 2018
Date:
2018-05
Bilbao, Spain
04-07 June 2018
Pagination:
6 p.
Published in:
Proceedings of the 2018 GIoTS
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2018-11-14, last modified 2019-06-11

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)