Publications

@article{INFFUSChacon2025,

title = { A multimodal hybrid deep learning approach for pest forecasting using time series and satellite images},

author = {A. M. Chacón-Maldonado and G. Asencio-Cortés and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S1566253525004233},

doi = {10.1016/j.inffus.2025.103350},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Information Fusion},

volume = {124},

pages = {103350},

keywords = {deep learning, precision agriculture},

pubstate = {published},

tppubtype = {article}

}

@article{Chacon2025,

title = {A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting},

author = {A. M. Chacón-Maldonado and L. Melgar-García and G. Asencio-Cortés and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s00521-024-10731-z},

doi = {https://doi.org/10.1007/s00521-024-10731-z},

year  = {2025},

date = {2025-01-01},

urldate = {2024-01-01},

journal = {Neural Computing and Applications},

keywords = {deep learning, precision agriculture, XAI},

pubstate = {published},

tppubtype = {article}

}

@article{TRONCOSO-GARCIA23b,

title = {Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting},

author = {A. R. Troncoso-García and I. S. Brito and A. Troncoso and F. Mártinez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0168169923007755},

doi = {https://doi.org/10.1016/j.compag.2023.108387},

year  = {2023},

date = {2023-11-08},

journal = {Computers and Electronics in Agriculture},

volume = {215},

pages = {108387},

abstract = {Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long shortterm memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time},

keywords = {deep learning, forecasting, precision agriculture, XAI},

pubstate = {published},

tppubtype = {article}

}