125280 20241125101150.0 doi 10.3390/plants12030633 sideral 132954 ART-2023-132954 eng Balduque Gil, J. (orcid)0000-0001-5136-8639 Big data and machine learning to improve european grapevine moth (Lobesia botrana) predictions 2023 Access copy available to the general public Unrestricted Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies. info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00 info:eu-repo/grantAgreement/ES/DGA/T17-20R info:eu-repo/grantAgreement/ES/DGA/T64-23R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.0 2023 PLANT SCIENCES 46 / 265 = 0.174 2023 Q1 T1 0.795 2023 Ecology 2023 Q1 Plant Science 2023 Q1 Ecology, Evolution, Behavior and Systematics 2023 Q1 6.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Lacueva Pérez, F. J. Labata Lezaun, G. Del Hoyo Alonso, R. Ilarri, S. Universidad de Zaragoza (orcid)0000-0002-7073-219X Sánchez Hernández, E. Martín Ramos, P. (orcid)0000-0003-2713-2786 Barriuso Vargas, J. J. Universidad de Zaragoza (orcid)0000-0003-2980-5454 5011 705 Universidad de Zaragoza Dpto. CC.Agrar.y Medio Natural Área Producción Vegetal 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 12, 3 (2023), 633 [16 pp.] Plants 2223-7747 2119356 http://zaguan.unizar.es/record/125280/files/texto_completo.pdf Versión publicada 2686588 http://zaguan.unizar.es/record/125280/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125280 articulos driver 2024-11-22-12:06:18 ARTICLE