Learning from Data to Optimize Control in Precision Farming

Alexander Kocian and Luca Incrocci
Additional contact information
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

Stats, 2020, vol. 3, issue 3, 1-7

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.

Keywords: statistics; precision agriculture; IoT; machine learning; reinforcement learning; water; production; soil; predictive analytics (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/3/3/18/pdf (application/pdf)
https://www.mdpi.com/2571-905X/3/3/18/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:3:y:2020:i:3:p:18-245:d:389437

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().