136050 20240711103551.0 doi 10.1016/j.compag.2018.05.007 sideral 106404 ART-2018-106404 eng Espejo-Garcia, Borja Universidad de Zaragoza (orcid)0000-0003-2733-680X Machine learning for automatic rule classification of agricultural regulations: A case study in Spain 2018 Currently, pest management practices require modern equipment and the use of complex information, such as regulations and guidelines. The complexity of regulations is the root cause of the emergence of automated solutions for compliance assessment by translating regulations into sets of machine-processable rules that can be run by specialized modules of farm management information systems (FMIS). However, the manual translation of rules is prohibitively costly, and therefore, this translation should be carried out with the support of artificial intelligence techniques. In this paper, we use the official Spanish phytosanitary products registry to empirically evaluate the performance of four popular machine learning algorithms in the task of correctly classifying pesticide regulations as prohibitions or obligations. Moreover, we also evaluate how to improve the performance of the algorithms in the preprocessing of the texts with natural language processing techniques. Finally, due to the specific characteristics of the texts found in pesticide regulations, resampling techniques are also evaluated. Experiments show that the combination of the machine learning algorithm Logic regression, the natural language technique part-of-speech tagging and the resampling technique Tomek links is the best performing approach, with an F1 score of 68.8%, a precision of 84.46% and a recall of 60%. The experimental results are promising, and they show that this approach can be applied to develop a computer-aided tool for transforming textual pesticide regulations into machine-processable rules. To the best of our knowledge, this is the first study that evaluates the use of artificial intelligence methods for the automatic translation of agricultural regulations into machine-processable representations. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MINECO/RTC-2016-4790-2 info:eu-repo/grantAgreement/ES/MINECO/TIN2017-88002-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 3.171 2018 AGRICULTURE, MULTIDISCIPLINARY 5 / 56 = 0.089 2018 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 31 / 106 = 0.292 2018 Q2 T1 0.95 2018 Agronomy and Crop Science 2018 Q1 Animal Science and Zoology 2018 Q1 Horticulture 2018 Q1 Forestry 2018 Q1 Computer Science Applications 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Martinez-Guanter, Jorge Pérez-Ruiz, Manuel Lopez-Pellicer, Francisco J. Universidad de Zaragoza (orcid)0000-0001-6491-7430 Zarazaga-Soria, F. Javier Universidad de Zaragoza (orcid)0000-0002-6557-2494 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 150 (2018), 343-352 Comput. electron. agric. Computers and Electronics in Agriculture 0168-1699 4169335 http://zaguan.unizar.es/record/136050/files/texto_completo.pdf Postprint 1663392 http://zaguan.unizar.es/record/136050/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:136050 articulos driver 2024-07-11-08:36:50 ARTICLE