Abstract
Agriculture plays an important role in Latin American countries where the demand for provisions to reduce hunger and poverty represents a significant priority in order to improve the development and quality of life in the region. In this research, linear data analysis techniques and soil classification are reviewed through neural networks for decision making in agriculture. The results permit to conclude that precision agriculture, observation and control technologies are gaining ground, making it possible to determine the production demand in these countries.
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Viloria, A., Ruiz-Lazaro, A., González, A.M.E., Lezama, O.B.P., Lamby, J., Castro, N.L. (2021). Prediction of the Efficiency for Decision Making in the Agricultural Sector Through Artificial Intelligence. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_91
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DOI: https://doi.org/10.1007/978-981-15-7234-0_91
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