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

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 & Schiffermuller) 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.

Automated summary

Machine Learning (ML) techniques are used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. This study focuses on optimizing the Touzeau model, a correlation model between temperature and L. botrana development, using data-driven ML models. Field observations and 30 GB of weather data were combined to train the ML models and make predictions on pest flights. The results show that ML models outperform the Touzeau model, with the best-performing model being an artificial neural network with four layers.