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Learning from Data to Optimize Control in Precision Farming

Author

Listed:
  • Alexander Kocian

    (Department of Computer Science, University of Pisa, 56127 Pisa, Italy)

  • Luca Incrocci

    (Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy)

Abstract

Precision farming is one way of many to meet a 55 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water resources. The catalyst for the emergence of precision farming has been satellite positioning and navigation followed by Internet-of-Things, generating vast information that can be used to optimize farming processes in real-time. Statistical tools from data mining, predictive modeling, and machine learning analyze patterns in historical data, to make predictions about future events as well as intelligent actions. This special issue presents the latest development in statistical inference, machine learning, and optimum control for precision farming.

Suggested Citation

  • Alexander Kocian & Luca Incrocci, 2020. "Learning from Data to Optimize Control in Precision Farming," Stats, MDPI, vol. 3(3), pages 1-7, July.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:3:p:18-245:d:389437
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    References listed on IDEAS

    as
    1. Gallardo, Marisa & Elia, Antonio & Thompson, Rodney B., 2020. "Decision support systems and models for aiding irrigation and nutrient management of vegetable crops," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Omolola M. Adisa & Joel O. Botai & Abiodun M. Adeola & Abubeker Hassen & Christina M. Botai & Daniel Darkey & Eyob Tesfamariam, 2019. "Application of Artificial Neural Network for Predicting Maize Production in South Africa," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
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