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Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning

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Hybrid Artificial Intelligent Systems (HAIS 2022)

Abstract

A new methodology has been applied to improve the prediction accuracy on the olive phenology forecasting problem, applying deep learning with hyperparameter optimization to handle with imbalanced data. The application of hyperparameter optimization to optimize the architecture of the deep neural network along with both class balancing preprocessing and the introduction of new variables allowed to improve the phenological forecast classification problem in 16 different plots from 4 different areas in Spain is introduced in this work. The results obtained have been shown to be promising and encourage further research in this field, where the potential for improvement is very high. The improvements, in terms of prediction accuracy, achieved are around 4% on average and, in some cases, exceeding 20%.

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Acknowledgements

The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the project PID2020-117954RB, the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516.

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Correspondence to Gualberto Asencio-Cortés .

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Chacón-Maldonado, A.M., Molina-Cabanillas, M.A., Troncoso, A., Martínez-Álvarez, F., Asencio-Cortés, G. (2022). Olive Phenology Forecasting Using Information Fusion-Based Imbalanced Preprocessing and Automated Deep Learning. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_24

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  • DOI: https://doi.org/10.1007/978-3-031-15471-3_24

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