Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

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© 2023 Mesías-Ruiz, Pérez-Ortiz, Dorado, de Castro and Peña. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Main Authors: Mesías-Ruiz, Gustavo A., Pérez-Ortiz, María, Dorado, José, Castro, Ana Isabel de, Peña Barragán, José Manuel
Other Authors: Agencia Estatal de Investigación (España)
Format: artículo de revisión biblioteca
Language:English
Published: Frontiers Media 2023-03-22
Subjects:Artificial intelligence, Decision support system (DDS), Deep learning, Precision agriculture, Robotics, Unmanned aerial vehicles,
Online Access:http://hdl.handle.net/10261/311175
http://dx.doi.org/10.13039/501100011033
https://api.elsevier.com/content/abstract/scopus_id/85153335582
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record_format koha
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
language English
topic Artificial intelligence
Decision support system (DDS)
Deep learning
Precision agriculture
Robotics
Unmanned aerial vehicles
Artificial intelligence
Decision support system (DDS)
Deep learning
Precision agriculture
Robotics
Unmanned aerial vehicles
spellingShingle Artificial intelligence
Decision support system (DDS)
Deep learning
Precision agriculture
Robotics
Unmanned aerial vehicles
Artificial intelligence
Decision support system (DDS)
Deep learning
Precision agriculture
Robotics
Unmanned aerial vehicles
Mesías-Ruiz, Gustavo A.
Pérez-Ortiz, María
Dorado, José
Castro, Ana Isabel de
Peña Barragán, José Manuel
Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
description © 2023 Mesías-Ruiz, Pérez-Ortiz, Dorado, de Castro and Peña. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
author2 Agencia Estatal de Investigación (España)
author_facet Agencia Estatal de Investigación (España)
Mesías-Ruiz, Gustavo A.
Pérez-Ortiz, María
Dorado, José
Castro, Ana Isabel de
Peña Barragán, José Manuel
format artículo de revisión
topic_facet Artificial intelligence
Decision support system (DDS)
Deep learning
Precision agriculture
Robotics
Unmanned aerial vehicles
author Mesías-Ruiz, Gustavo A.
Pérez-Ortiz, María
Dorado, José
Castro, Ana Isabel de
Peña Barragán, José Manuel
author_sort Mesías-Ruiz, Gustavo A.
title Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
title_short Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
title_full Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
title_fullStr Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
title_full_unstemmed Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review
title_sort boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: a contextual review
publisher Frontiers Media
publishDate 2023-03-22
url http://hdl.handle.net/10261/311175
http://dx.doi.org/10.13039/501100011033
https://api.elsevier.com/content/abstract/scopus_id/85153335582
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spelling dig-inia-es-10261-3111752024-10-27T21:49:47Z Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review Mesías-Ruiz, Gustavo A. Pérez-Ortiz, María Dorado, José Castro, Ana Isabel de Peña Barragán, José Manuel Agencia Estatal de Investigación (España) Ministerio de Educación y Formación Profesional (España) Mesías-Ruiz, Gustavo A. [0000-0002-3774-4121] Pérez-Ortiz, María [0000-0003-1302-6093] Dorado, José [0000-0002-2268-2562] Castro, Ana Isabel de [0000-0002-6699-2204] Peña Barragán, José Manuel [0000-0003-4592-3792] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] Artificial intelligence Decision support system (DDS) Deep learning Precision agriculture Robotics Unmanned aerial vehicles © 2023 Mesías-Ruiz, Pérez-Ortiz, Dorado, de Castro and Peña. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks. This work was supported by the Spanish Research State Agency (AEI) through the Projects PDC2021-121537-C22/AEI/10.13039/501100011033 and PID2020-113229RB-C41. The lead author GM-R has been a beneficiary of a FPI fellowship by the Spanish Ministry of Education and Professional Training (PRE2018-083227). Peer reviewed 2023-06-12T07:40:47Z 2023-06-12T07:40:47Z 2023-03-22 artículo de revisión http://purl.org/coar/resource_type/c_dcae04bc Frontiers in Plant Science 14: e1143326 (2023) 1664-462X http://hdl.handle.net/10261/311175 10.3389/fpls.2023.1143326 http://dx.doi.org/10.13039/501100011033 37056493 2-s2.0-85153335582 https://api.elsevier.com/content/abstract/scopus_id/85153335582 en #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI//PDC2021-121537-C22 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113229RB-C41/ES/DESARROLLO Y VALIDACION DE NUEVAS TECNOLOGIAS DE TELEDETECCION Y APRENDIZAJE AUTOMATICO APLICADAS AL CONTROL INTELIGENTE DE MALAS HIERBAS/ info:eu-repo/grantAgreement/MEFP//PRE2018-083227 Departamento de Medio Ambiente y Agronomía​ Publisher's version https://doi.org/10.3389/fpls.2023.1143326 Sí open application/pdf Frontiers Media

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