Literature DB >> 33322252

Exploring the Adoption of Precision Agriculture for Irrigation in the Context of Agriculture 4.0: The Key Role of Internet of Things.

Sergio Monteleone1, Edmilson Alves de Moraes1, Brenno Tondato de Faria2, Plinio Thomaz Aquino Junior2, Rodrigo Filev Maia3, André Torre Neto4, Attilio Toscano5.   

Abstract

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer's possible choice to adopt several IoT sensing technologies for data collection.

Entities:  

Keywords:  Internet of things; adoption; agriculture 4.0; farmer behavior; irrigation; operations management; precision agriculture; satellite; sensing technologies; weather station

Mesh:

Year:  2020        PMID: 33322252      PMCID: PMC7763172          DOI: 10.3390/s20247091

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  14 in total

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Review 3.  Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

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4.  Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling.

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Journal:  Sensors (Basel)       Date:  2018-10-11       Impact factor: 3.576

5.  Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils.

Authors:  J D Jabro; W B Stevens; W M Iversen; B L Allen; U M Sainju
Journal:  Sensors (Basel)       Date:  2020-02-29       Impact factor: 3.576

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Journal:  Sensors (Basel)       Date:  2019-09-30       Impact factor: 3.576

7.  Impact of Climate Change on Irrigation Demand and Crop Growth in a Mediterranean Environment of Turkey.

Authors:  Tomohisa Yano; Mehmet Aydin; Tomokazu Haraguchi
Journal:  Sensors (Basel)       Date:  2007-10-16       Impact factor: 3.576

8.  Assessing the Crop-Water Status in Almond (Prunus dulcis Mill.) Trees via Thermal Imaging Camera Connected to Smartphone.

Authors:  Iván Francisco García-Tejero; Carlos José Ortega-Arévalo; Manuel Iglesias-Contreras; José Manuel Moreno; Luciene Souza; Simón Cuadros Tavira; Víctor Hugo Durán-Zuazo
Journal:  Sensors (Basel)       Date:  2018-03-31       Impact factor: 3.576

9.  Defining a Cyber Resilience Investment Strategy in an Industrial Internet of Things Context.

Authors:  Juan Francisco Carías; Leire Labaka; José María Sarriegi; Josune Hernantes
Journal:  Sensors (Basel)       Date:  2019-01-03       Impact factor: 3.576

10.  Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

Authors:  Yu Zhang; Wenting Han; Xiaotao Niu; Guang Li
Journal:  Sensors (Basel)       Date:  2019-11-29       Impact factor: 3.576

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