Big data and machine learning to improve european grapevine moth (Lobesia botrana) predictions

Balduque Gil, J. ; Lacueva Pérez, F. J. ; Labata Lezaun, G. ; Del Hoyo Alonso, R. ; Ilarri, S. (Universidad de Zaragoza) ; Sánchez Hernández, E. ; Martín Ramos, P. ; Barriuso Vargas, J. J. (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.3390/plants12030633

Año: 2023

Publicado en: Plants 12, 3 (2023), 633 [16 pp.]
ISSN: 2223-7747

Factor impacto JCR: 4.0 (2023)

Categ. JCR: PLANT SCIENCES rank: 46 / 265 = 0.174 (2023) - Q1 - T1

Factor impacto CITESCORE: 6.5 - Ecology, Evolution, Behavior and Systematics (Q1) - Plant Science (Q1) - Ecology (Q1)

Factor impacto SCIMAGO: 0.795 - Ecology (Q1) - Plant Science (Q1) - Ecology, Evolution, Behavior and Systematics (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113037RB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T17-20R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T64-23R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Producción Vegetal (Dpto. CC.Agrar.y Medio Natural)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)