000136051 001__ 136051
000136051 005__ 20240711103551.0
000136051 0247_ $$2doi$$a10.1016/j.compag.2019.03.027
000136051 0248_ $$2sideral$$a111427
000136051 037__ $$aART-2019-111427
000136051 041__ $$aeng
000136051 100__ $$0(orcid)0000-0003-2733-680X$$aEspejo-Garcia, Borja$$uUniversidad de Zaragoza
000136051 245__ $$aEnd-to-end sequence labeling via deep learning for automatic extraction of agricultural regulations
000136051 260__ $$c2019
000136051 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136051 5203_ $$aIn the European Union, production standards in the form of legal regulations play an important role in farming. Because of the increasing amount of regulations, it is desirable to transform human-oriented regulations into a set of computer-oriented rules to provide decision support through the Farm Management Information System. To obtain the logical structure of rules, automatically labeling their meaningful information is necessary. In this work, we evaluate the performance of 8 different state-of-the-art deep learning architectures to develop an end-to-end sequence labeler for phytosanitary regulations. This sequence labeler extracts different meaningful information items to determine which pesticides can be applied to a crop, the place of the treatment, when it can be applied, and the maximum number of applications. The architectures evaluated do not require feature engineering and, hence, they are applicable to the agricultural regulations of different countries. The best system is a neural network that uses character embeddings, Bidirectional Long short-term memory and Softmax. It achieves a performance of 88.3% F 1 score.
000136051 536__ $$9info:eu-repo/grantAgreement/ES/DGA/C38-2015$$9info:eu-repo/grantAgreement/ES/MINECO/RTC-2016-4790-2$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-88002-R
000136051 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000136051 590__ $$a3.858$$b2019
000136051 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b24 / 109 = 0.22$$c2019$$dQ1$$eT1
000136051 591__ $$aAGRICULTURE, MULTIDISCIPLINARY$$b5 / 58 = 0.086$$c2019$$dQ1$$eT1
000136051 592__ $$a1.058$$b2019
000136051 593__ $$aAgronomy and Crop Science$$c2019$$dQ1
000136051 593__ $$aAnimal Science and Zoology$$c2019$$dQ1
000136051 593__ $$aHorticulture$$c2019$$dQ1
000136051 593__ $$aForestry$$c2019$$dQ1
000136051 593__ $$aComputer Science Applications$$c2019$$dQ1
000136051 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000136051 700__ $$0(orcid)0000-0001-6491-7430$$aLopez-Pellicer, Francisco J.$$uUniversidad de Zaragoza
000136051 700__ $$0(orcid)0000-0003-3071-5819$$aLacasta, Javier$$uUniversidad de Zaragoza
000136051 700__ $$0(orcid)0000-0001-5311-750X$$aPiedrafita Moreno, Ramón$$uUniversidad de Zaragoza
000136051 700__ $$0(orcid)0000-0002-6557-2494$$aZarazaga-Soria, F. Javier$$uUniversidad de Zaragoza
000136051 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000136051 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000136051 773__ $$g162 (2019), 106-111$$pComput. electron. agric.$$tComputers and Electronics in Agriculture$$x0168-1699
000136051 8564_ $$s576254$$uhttps://zaguan.unizar.es/record/136051/files/texto_completo.pdf$$yPostprint
000136051 8564_ $$s1369104$$uhttps://zaguan.unizar.es/record/136051/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000136051 909CO $$ooai:zaguan.unizar.es:136051$$particulos$$pdriver
000136051 951__ $$a2024-07-11-08:36:54
000136051 980__ $$aARTICLE