RT info:eu-repo/semantics/article
T1 Big data and machine learning to improve European grapevine moth (Lobesia botrana) predictions
A1 Balduque Gil, Joaquín
A1 Lacueva Pérez, Francisco J.
A1 Labata Lezaun, Gorka
A1 Ilarri, Sergio
A1 Del Hoyo Alonso, Rafael
A1 Sánchez Hernández, Eva
A1 Martín Ramos, Pablo
A1 Barriuso Vargas, Juan José
K1 Lobesia botrana
K1 Grapes - Diseases and pests
K1 Vid - Enfermedades y plagas
K1 Pesticides - Application
K1 Control de plagas
K1 Big data
K1 Internet of things
K1 Internet de las cosas
K1 Internet - Tecnología
K1 Meteorology
K1 Weather data
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 Pests - Integrated control
K1 Manejo integrado de plagas
K1 Plant Science
K1 Ecology
K1 Ecología
K1 3102 Ingeniería Agrícola
K1 2509.01 Meteorología agrícola
K1 3101.09 Plaguicidas
K1 1203.04 Inteligencia Artificial
AB 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.
PB MDPI
SN 2223-7747
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/63485
UL https://uvadoc.uva.es/handle/10324/63485
LA eng
NO Plants, 2023, Vol. 12, Nº. 3, 633
NO Producción Científica
DS UVaDOC
RD 17-jul-2024