000125280 001__ 125280
000125280 005__ 20241125101150.0
000125280 0247_ $$2doi$$a10.3390/plants12030633
000125280 0248_ $$2sideral$$a132954
000125280 037__ $$aART-2023-132954
000125280 041__ $$aeng
000125280 100__ $$0(orcid)0000-0001-5136-8639$$aBalduque Gil, J.
000125280 245__ $$aBig data and machine learning to improve european grapevine moth (Lobesia botrana) predictions
000125280 260__ $$c2023
000125280 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125280 5203_ $$aMachine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
000125280 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T17-20R$$9info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T64-23R
000125280 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125280 590__ $$a4.0$$b2023
000125280 592__ $$a0.795$$b2023
000125280 591__ $$aPLANT SCIENCES$$b46 / 265 = 0.174$$c2023$$dQ1$$eT1
000125280 593__ $$aEcology$$c2023$$dQ1
000125280 593__ $$aPlant Science$$c2023$$dQ1
000125280 593__ $$aEcology, Evolution, Behavior and Systematics$$c2023$$dQ1
000125280 594__ $$a6.5$$b2023
000125280 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125280 700__ $$aLacueva Pérez, F. J.
000125280 700__ $$aLabata Lezaun, G.
000125280 700__ $$aDel Hoyo Alonso, R.
000125280 700__ $$0(orcid)0000-0002-7073-219X$$aIlarri, S.$$uUniversidad de Zaragoza
000125280 700__ $$aSánchez Hernández, E.
000125280 700__ $$0(orcid)0000-0003-2713-2786$$aMartín Ramos, P.
000125280 700__ $$0(orcid)0000-0003-2980-5454$$aBarriuso Vargas, J. J.$$uUniversidad de Zaragoza
000125280 7102_ $$15011$$2705$$aUniversidad de Zaragoza$$bDpto. CC.Agrar.y Medio Natural$$cÁrea Producción Vegetal
000125280 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000125280 773__ $$g12, 3 (2023), 633 [16 pp.]$$tPlants$$x2223-7747
000125280 8564_ $$s2119356$$uhttps://zaguan.unizar.es/record/125280/files/texto_completo.pdf$$yVersión publicada
000125280 8564_ $$s2686588$$uhttps://zaguan.unizar.es/record/125280/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125280 909CO $$ooai:zaguan.unizar.es:125280$$particulos$$pdriver
000125280 951__ $$a2024-11-22-12:06:18
000125280 980__ $$aARTICLE