Grapevine Phenology Prediction: A Comparison of Physical and Machine Learning Models

Lacueva-Pérez, Francisco José; Ilarri, Sergio; Barriuso, J.; Balduque-Gil, Joaquín; Labata-Lezaun, Gorka; del-Hoyo-Alonso, Rafael

doi:10.1007/978-3-031-12670-3_24

Cited by 1 publication

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on results from previous work [ 60 , 61 ], an ANN was selected for training, but other more complex models could be trained to see if they would be able to obtain better results.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 4 (left) shows the location of the monitoring sites. Data from the cadaster of the Spanish registry [ 65 ] and the Aragon Open Data portal [ 66 ] provided the geographical description of the monitoring points, also allowing, through their location, to link each point to the nearest weather station as well as to information related to their geopositioning [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

“…For our study, and based on the results presented in previous work [ 60 ], we created a model using an ANN. The aim of the model obtained was to predict the probability that a given date, within a 2-week horizon, is the day of flight peak for each of the generations of L. botrana in a given season.…”

Section: Methodsmentioning

confidence: 99%

“…The models were trained using Scikit-learn [ 80 ], Keras [ 81 ], and Optuna [ 82 ] on Python [ 83 ]. As noted above, based on results from previous work [ 60 ], an ANN was chosen for training. The selection of the data for testing and validation was performed considering data from different monitoring sites over different years during the study period.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions

Balduque-Gil

Lacueva-Pérez

Labata-Lezaun

et al. 2023

Plants

View full text Add to dashboard Cite

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.

show abstract

“…Based on results from previous work [ 60 , 61 ], an ANN was selected for training, but other more complex models could be trained to see if they would be able to obtain better results.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations