Last update: July 2024
DREAM: Driving Research for Enhanced Agriculture with Machine Learning
Brief description
Agriculture is a key component of the primary sector of the economy and an essential activity to ensure the provision of food for people, particularly important in difficult times. With DREAM (Driving Research for Enhanced Agriculture with Machine Learning) we aim at the development of data management methodologies and test machine learning approaches to predict the phenological state of plants (fruit crops). Our final goal is to foster a suitable behavior of farmers regarding the actions that they should perform in order to prevent pests from spoiling the crops but at the same time optimize the use of treatments (avoiding unneeded treatments, which could lead to environmental and economic damage with no benefit).
The expected contributions to the agriculture sector can help to achieve SDG number 2 ("End hunger, achieve food security and improved nutrition and promote sustainable agriculture"), by ensuring the sustainability of farmlands and by providing predictions during crisis periods like virus outbreaks.
Dataset
Dataset used in our paper submitted to the Computers and Electronics in Agriculture journal (under review) (password-protected; access must be requested)
Software
Main Related Publications
Joaquín Balduque-Gil, Francisco J. Lacueva-Pérez, Gorka Labata-Lezaun, Rafael del-Hoyo-Alonso, Sergio Ilarri, Eva Sánchez-Hernández, Pablo Martín-Ramos, Juan J. Barriuso-Vargas, "Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions", Plants, ISSN 2223-7747, volume 12, issue 3, 633, 16 pp., MDPI AG, February 2023. Topic Collection "Application of AI in Plants".
(DOI: 10.3390/plants12030633)
Francisco José Lacueva-Pérez, Sergio Ilarri, Juan José Barriuso Vargas, Joaquín Balduque, Gorka Labata, Rafael del-Hoyo,
"Grapevine Phenology Prediction: A Comparison of Physical and Machine Learning Models",
24th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2022), Vienna (Austria), Springer, Lecture Notes in Computer Science (LNCS), volume 13428, ISSN 0302-9743, ISSN 1611-3349 (electronic), Print ISBN 978-3-031-12669-7, Online ISBN 978-3-031-12670-3, pp. 263-269, August 2022.
(DOI: 10.1007/978-3-031-12670-3_24)
Other Previous Related Publications
Francisco José Lacueva-Pérez, Sergio Ilarri, Juan José Barriuso Vargas, Gorka Labata Lezaun, Rafael del Hoyo Alonso,
"Multifactorial Evolutionary Prediction of Phenology and Pests: Can Machine Learning Help?",
16th International Conference on Web Information Systems and Technologies (WEBIST 2020), SCITEPRESS, ISBN 978-989-758-478-7, volume 1, position paper, pp. 75-82, November 2020. Selected in a short list of candidates to win the WEBIST 2020 best industrial paper award.
Online conference due to the COVID-19 pandemic.
(DOI: 10.5220/0010132900750082)
Francisco José Lacueva, Rafael Del Hoyo, Juan José Barriuso, Sergio Ilarri,
"Towards Improving Agriculture Sustainability through Multifactorial Machine Learning", Actas de la IX Jornada de Jóvenes Investigadores del I3A, ISSN 2341-4790, volumen 8, Instituto Universitario de Investigación en Ingeniería de Aragón, 2 pp., diciembre de 2020.
Poster
Contributors
Main contributors
Acknowledgments
- Project PID2020-113037RB-I00 / AEI / 10.13039/501100011033 — Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges (NEAT-AMBIENCE).
- Government of Aragon (COSMOS research group and IODIDE research group; last group reference of COSMOS: T64_23R; previous group reference of COSMOS: T64_20R).
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