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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)


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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  1. Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021)

    Article  MathSciNet  Google Scholar 

  2. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: a review. Sensors 18(8), 2674 (2018)

    Article  Google Scholar 

  3. Molina, M.Á., Jiménez-Navarro, M.J., Martínez-Álvarez, F., Asencio-Cortés, G.: A model-based deep transfer learning algorithm for phenology forecasting using satellite imagery. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds.) HAIS 2021. LNCS (LNAI), vol. 12886, pp. 511–523. Springer, Cham (2021).

    Chapter  Google Scholar 

  4. Yang, Q., Shi, L., Han, J., Yu, J., Huang, K.: A near real-time deep learning approach for detecting rice phenology based on UAV images. Agric. For. Meteorol. 287, 107938 (2020)

    Article  Google Scholar 

  5. Yalcin, H.: Phenology recognition using deep learning. In: Proceedings of the Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting, pp. 1–5 (2018)

    Google Scholar 

  6. Grünig, M., Razavi, E., Calanca, P., Mazzi, D., Wegner, J.D., Pellissier, L.: Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Emerg. Technol. 12, e03791 (2021)

    Google Scholar 

  7. Skakun, S., et al.: Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model. Remote Sens. Environ. 195, 244–258 (2017)

    Article  Google Scholar 

  8. Hao, P., Zhan, Y., Wang, L., Niu, Z., Shakir, M.: Feature selection of time series MODIS data for early crop classification using random forest: a case study in Kansas, USA. Remote Sens. 7(5), 5347–5369 (2015)

    Article  Google Scholar 

  9. Wang, Y., Xue, Z., Chen, J., Chen, G.: Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015. Front. Earth Sci. 13(1), 92–110 (2019).

    Article  Google Scholar 

  10. Xue, Z., Du, P., Feng, L.: Phenology-driven land cover classification and trend analysis based on long-term remote sensing image series. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7(4), 1142–1156 (2014)

    Article  Google Scholar 

  11. Melgar, L., Gutiérrez-Avilés, D., Godinho, M.T., et al.: A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing 500, 268–278 (2022)

    Article  Google Scholar 

  12. Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.H.: Deep learning for person re-identification: a survey and outlook. CoRR, abs/2001.04193 (2020)

    Google Scholar 

  13. Feng, S., Zhou, H., Dong, H.: Using deep neural network with small dataset to predict material defects. Mater. Des. 162, 300–310 (2019)

    Article  Google Scholar 

  14. Torres, J.F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., Troncoso, A.: Deep learning for time series forecasting: a survey. Big Data 9, 3–21 (2021)

    Article  Google Scholar 

  15. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  16. Branco, P., Torgo, L., Ribeiro, R.: A survey of predictive modelling under imbalanced domains. ACM Comput. Surv. 49(a30), 1–50 (2017)

    Google Scholar 

  17. He, H., Bai, Y., Garcia, E. A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1322–1328 (2008)

    Google Scholar 

  18. Nguyen, H.M., Cooper, E.W., Kamei, K.: Borderline over-sampling for imbalanced data classification. Int. J. Knowl. Eng. Soft Data Paradig. 3(1), 4–21 (2011)

    Article  Google Scholar 

  19. de Andalucia, J.: RAIF website of the Consejeria de Agricultura, pesca y desarrollo rural (2020). Accessed 26 Mar 2020

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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.

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