RT Journal Article
T1 A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
A1 Chacón Maldonado, Andrés Manuel
A1 Melgar García, Laura
A1 Asencio Cortés, Gualberto
A1 Troncoso, Alicia
K1 Deep learning
K1 Explainable artificial intelligence
K1 Agriculture
AB Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencodernetwork improves the accuracy of predictions.
PB Springer
YR 2024
FD 2024-12-12
LK https://hdl.handle.net/10433/22170
UL https://hdl.handle.net/10433/22170
LA en
NO Chacón-Maldonado, A.M., Melgar-García, L., Asencio-Cortés, G. et al. A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-10731-z
NO Data Science and Big Data Lab
DS RIO
RD May 9, 2026