Statistical Tools in Precision Farming

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 3385

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, IT-56127 Pisa, Italy
Interests: foundations of probability and statistics; machine learning; analog and digital signal processing; ambient intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy
Interests: greenhouse horticulture; soilless culture; nursery crops; plant water relations; mineral nutrition; crop modeling; quality of vegetables; open-field vegetable crops; biofortification; wild edible plants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to a McKinsey and company report in 2016, nearly 800 million people go hungry, while one third of all food is lost during production each year. To tackle this problem, research has focused on precision farming during the last few years, where productivity, efficiency, and profitability are increased on a long-term basis while unintended impacts on the environment are minimized.

This Special Issue focuses on theories, methodologies, techniques, and tools in statistics that improve state-of-the-art precision farming. Topics include but are not limited to statistical learning from sensor data, statistical analysis of data, prediction of performance indicators, and statistical reasoning.

Dr. Alexander Kocian
Assoc. Prof. Dr. Luca Incrocci
Guest Editors

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Keywords

  • precision agriculture
  • IoT
  • regression
  • classification
  • clustering
  • time-series
  • machine learning
  • reinforcement learning
  • stochastic control

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Published Papers (1 paper)

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Editorial

7 pages, 198 KiB  
Editorial
Learning from Data to Optimize Control in Precision Farming
by Alexander Kocian and Luca Incrocci
Stats 2020, 3(3), 239-245; https://doi.org/10.3390/stats3030018 - 24 Jul 2020
Cited by 11 | Viewed by 3097
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Statistical Tools in Precision Farming)
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