Artificial Intelligence (AI) in Agriculture

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (8 June 2021) | Viewed by 2805

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Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004 Ourense, Spain
Interests: artificial intelligence (neural networks, fuzzy logic, expert systems, etc.); physical chemistry; water management; hydrology; food technology; bioinformatics; palynology
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Special Issue Information

Dear Colleagues,

For the past few years, the increasing world population has given place to an increase in the demand for food products.

A large number of variables (agronomic, climatic, political, economic, etc.) can influence on agricultural production. All these features give rise to a large database that can be used to develop tools aimed at improving the management practices, production, harvesting, processing, conservation, selling and subsequent waste treatment that could solve the future challenges related to the climate variation, proliferation of diseases, crops improve and supply.

These tools, from the simplest (regression) to the most complex (neural networks, vector support machines, among others) allow to expand the existing knowledge to the entire agricultural process (from cradle to cradle).

The aim of this Special Issue about the "Artificial Intelligence (AI) in Agriculture" is to collect the most recent research using any kind of AI model related (but not limited) to: machine learning, remote sensing, machine vision, modelling, prediction, optimization, decision support, food authenticity, big data, blockchain, etc.

You are welcome to send research articles, reviews, communications and concept papers.

Dr. Gonzalo Astray Dopazo
Guest Editor

Manuscript Submission Information

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  • Artificial Intelligence
  • Machine learning
  • Deep learning
  • Image Analysis/Processing
  • Computer Vision
  • Internet of Things (IoT)
  • Big Data/Cloud Computing
  • Remote Sensing
  • Modelling/Prediction/Optimization
  • Decision support

Published Papers (1 paper)

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16 pages, 484 KiB  
Digital Irrigated Agriculture: Towards a Framework for Comprehensive Analysis of Decision Processes under Uncertainty
by Francesco Cavazza, Francesco Galioto, Meri Raggi and Davide Viaggi
Future Internet 2020, 12(11), 181; - 26 Oct 2020
Cited by 4 | Viewed by 2115
Several studies address the topic of Information and Communication Technologies (ICT) adoption in irrigated agriculture. Many of these studies testify on the growing importance of ICT in influencing the evolution of the sector, especially by bringing down information barriers. While the potentialities of [...] Read more.
Several studies address the topic of Information and Communication Technologies (ICT) adoption in irrigated agriculture. Many of these studies testify on the growing importance of ICT in influencing the evolution of the sector, especially by bringing down information barriers. While the potentialities of such technologies are widely investigated and confirmed, there is still a gap in understanding and modeling decisions on ICT information implementation. This gap concerns, in particular, accounting for all the aspects of uncertainty which are mainly due to a lack of knowledge on the reliability of ICT and on the errors of ICT-information. Overall, such uncertainties might affect Decision Makers’ (DM’s) behavior and hamper ICT uptake. To support policy makers in the designing of uncertainty-management policies for the achievement of the benefits of a digital irrigated agriculture, we further investigated the topic of uncertainty modelling in ICT uptake decisions. To do so, we reviewed the economic literature on ambiguity, in the context of the wider literature on decision making under uncertainty in order to explore its potential for better modeling ICT uptake decisions. Findings from the discussed literature confirm the capabilities of this approach to yield a deeper understanding of decision processes when the reliability of ICT is unknown and provides better insights on how behavioral barriers to the achievement of potential ICT-benefits can be overcome. Policy implications to accompany the sector in the digitalization process include mainly: (a) defining new approaches for ICT-developers to tailor platforms to answer heterogeneous DMs’ needs; (b) establish uncertainty-management policies complementary to DM tools adoption. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Agriculture)
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