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Authors: Francisco Lacueva-Pérez 1 ; Sergio Ilarri 2 ; 3 ; Juan Vargas 4 ; 5 ; Gorka Lezaun 1 and Rafael Alonso 1

Affiliations: 1 ITAINNOVA - Instituto Tecnológico de Aragón, PT. Walqa, Huesca, Spain ; 2 Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain ; 3 I3A, University of Zaragoza, Zaragoza, Spain ; 4 AgriFood Institute of Aragon (IA2), Zaragoza, Spain ; 5 CITA - Universidad de Zaragoza, Zaragoza, Spain

Keyword(s): Smart Farming, Phenology Forecast, Machine Learning, Big Data, Remote Sensing.

Abstract: Agriculture is a key primary sector of economy. Developing and applying techniques that support a sustainable development of the fields and maximize their productivity, while guaranteeing the maximum levels of health and quality of the crops, is necessary. Precision agriculture refers to the use of technology to help in the decision-making process and can lead to the achievement of these goals. In this position paper, we argue that machine learning (ML) techniques can provide significant benefits to precision agriculture, but that there exist obstacles that are preventing their widespread adoption and effective application. Particularly, we focus on the prediction of phenology changes and pests, due to their important to ensure the quality of the crops. We analyze the state of the art, present the existing challenges, and outline our specific research goals.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Lacueva-Pérez, F.; Ilarri, S.; Vargas, J.; Lezaun, G. and Alonso, R. (2020). Multifactorial Evolutionary Prediction of Phenology and Pests: Can Machine Learning Help?. In Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST, ISBN 978-989-758-478-7; ISSN 2184-3252, pages 75-82. DOI: 10.5220/0010132900750082

@conference{webist20,
author={Francisco Lacueva{-}Pérez. and Sergio Ilarri. and Juan Vargas. and Gorka Lezaun. and Rafael Alonso.},
title={Multifactorial Evolutionary Prediction of Phenology and Pests: Can Machine Learning Help?},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST,},
year={2020},
pages={75-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010132900750082},
isbn={978-989-758-478-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - WEBIST,
TI - Multifactorial Evolutionary Prediction of Phenology and Pests: Can Machine Learning Help?
SN - 978-989-758-478-7
IS - 2184-3252
AU - Lacueva-Pérez, F.
AU - Ilarri, S.
AU - Vargas, J.
AU - Lezaun, G.
AU - Alonso, R.
PY - 2020
SP - 75
EP - 82
DO - 10.5220/0010132900750082

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