2026-04-18T13:54:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/63882022-03-23T10:39:47Zcom_10259_3847com_10259_5086com_10259_2604com_10259_5495col_10259_3848col_10259_5496

Yartu González, Mercedes Esther de a6d86fa3-a76d-4575-b5b6-5f22d9e119cd Cambra Baseca, Carlos 78d329db-270f-4325-8b8f-cbdbc61c53ce Navarro González, Milagros 390 600 Rad Moradillo, Juan Carlos 461 600 Arroyo Puente, Ángel 2b748f96-3376-466e-8fe5-db2eac9c093a Herrero, Alvaro 283 600 0000-0002-2444-5384 2022-02-02T10:20:49Z 2022-02-02T10:20:49Z 2022-03 1877-7503 http://hdl.handle.net/10259/6388 10.1016/j.jocs.2021.101547 It is widely acknowledged that, under the frame of sustainable farming, using the minimum water resources is a relevant requirement. In order to do that, precision irrigation aims at identifying the irrigation needs of plantations and irrigate accordingly. Artificial intelligence is a promising solution in this field as intelligent models are able to learn the soil moisture dynamics in the soil-plant-atmosphere system and then generating appropriate irrigation scheduling. This is a complex task as the phenology of plants and its water demand vary with soil properties and weather conditions. The present research contributes to this challenging task by proposing the application of neural networks in order to learn the time-series evolution of irrigation needs associated to a potato plantation. Several of such models are thoroughly compared, together with different interpolation methods, in order to find the best combination for accurately forecasting water needs. In order to predict the soil water content in a potato field crop, in which soil humidity probes were installed at 15, 30, and 45 cm depth during the whole cycle of a potato crop. This innovative study and its promising results provide with significant contributions to address the problem of predicting and managing groundwater for agricultural use in a sustainable way. Lab-Ferrer (METER Group) and the UBUCOMP research group at the University of Burgos. application/pdf eng Elsevier Journal of Computational Science. 2022, V. 59, 101547 https://doi.org/10.1016/j.jocs.2021.101547 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Precision irrigation Potato crop Time series forecast Supervised learning Neural networks Interpolation Agricultura Informática Agriculture Computer science Humidity forecasting in a potato plantation using time-series neural models info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Journal of Computational Science 59 101547 THUMBNAIL Yartu-jcs_2022.pdf.jpg Yartu-jcs_2022.pdf.jpg IM Thumbnail image/jpeg 4946 https://riubu.ubu.es/bitstream/10259/6388/4/Yartu-jcs_2022.pdf.jpg 5c8496fbd1ce8cd63f9c47da74d6113a MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/6388/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/6388/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Yartu-jcs_2022.pdf Yartu-jcs_2022.pdf application/pdf 2266283 https://riubu.ubu.es/bitstream/10259/6388/1/Yartu-jcs_2022.pdf 4d0458e0514824ddb24ba6482dc59494 MD5 1 10259/6388 oai:riubu.ubu.es:10259/6388 2022-03-23 11:39:47.366 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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