Citation: Nasirahmadi, A.; Hensel, O.
Toward the Next Generation of
Digitalization in Agriculture Based
on Digital Twin Paradigm. Sensors
2022, 22, 498. https://doi.org/
10.3390/s22020498
Academic Editors: Dionysis Bochtis
and Aristotelis C. Tagarakis
Received: 6 December 2021
Accepted: 7 January 2022
Published: 10 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Review
Toward the Next Generation of Digitalization in Agriculture
Based on Digital Twin Paradigm
Abozar Nasirahmadi *
and Oliver Hensel
Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany;
agrartechnik@uni-kassel.de
* Correspondence: abozar.nasirahmadi@uni-kassel.de
Abstract:
Digitalization has impacted agricultural and food production systems, and makes applica-
tion of technologies and advanced data processing techniques in agricultural field possible. Digital
farming aims to use available information from agricultural assets to solve several existing challenges
for addressing food security, climate protection, and resource management. However, the agricultural
sector is complex, dynamic, and requires sophisticated management systems. The digital approaches
are expected to provide more optimization and further decision-making supports. Digital twin in
agriculture is a virtual representation of a farm with great potential for enhancing productivity and
efficiency while declining energy usage and losses. This review describes the state-of-the-art of
digital twin concepts along with different digital technologies and techniques in agricultural con-
texts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries,
and food post-harvest processing in agricultural field. Data recording, modeling including artificial
intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet
of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems
can support farmers as a next generation of digitalization paradigm by continuous and real-time
monitoring of physical world (farm) and updating the state of virtual world.
Keywords: digital twin; digitalization; digital farming; farm management; smart farming
1. Introduction
One of the main global challenges is how to ensure food security for the world’s
growing population whilst ensuring long-term sustainable development. According to the
Food and Agriculture Organization, agricultural and food productions will need to grow
to feed the world population, which will reach around 10 billion by 2050 [
1
]. Due to the
increase in world population and market demand for higher product quantity and quality
standards, the issue of food security, sustainability, productivity, and profitability becomes
more important. Furthermore, the economic pressure on the agricultural sector, labor,
environmental, and climate change issues are increasing [
2
,
3
]. Therefore, the enhancement
of efficiency through effective integrated smart technologies and techniques has been
widely considered in recent years.
In this context, digital agriculture (also known as smart farming or smart agriculture)
tools can support the deeper understanding of interrelations within the agricultural pro-
duction system and the consequent effects on the performance of farm production while
balancing human health and well-being, social and environmental aspects, and sustain-
ability associated with agricultural system [
4
–
6
]. Due to advances in data generation, data
processing and human-computer interactions, digital farming has progressed in recent
years [
7
]. One of the main features of digitalization in agriculture is the introduction of inno-
vative Information and Communication Technology (ICT), Internet of Things (IoT), big data
analytics and interpretation techniques, machine learning and Artificial Intelligence (AI).
Data acquisition and analysis in digital farming by means of smart technologies are
supporting complex decision-making approaches [
8
,
9
]. They enhance final productivity,
Sensors 2022, 22, 498. https://doi.org/10.3390/s22020498 https://www.mdpi.com/journal/sensors
