Blockchain Applications in Agriculture: A Scoping Review

Abstract

Blockchain is a distributed, immutable ledger technology initially developed to secure cryptocurrency transactions. Following its revolutionary use in cryptocurrencies, blockchain solutions are now being proposed to address various problems in different domains, and it is currently one of the most “disruptive” technologies. This paper presents a scoping review of the scientific literature for exploring the current research area of blockchain applications in the agricultural sector. The aim is to identify the service areas of agriculture where blockchain is used, the blockchain technology used, the data stored in it, its combination with external databases, the reason it is used, and the variety of agricultural products, as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR methodology. The purpose of conducting these scoping reviews is to identify the evidence in this field and clarify the key concepts. The literature search was conducted in April 2021 using Scopus and Google Scholar, and a systematic selection process identified 104 research articles for detailed study. Our findings show that in the field, although still in the early stages, with the majority of the studies in the design phase, several experiments have been conducted, so a significant percentage of the work is in the implementation or piloting phase. Finally, our research shows that the use of blockchain in this domain mainly concerns the integrity of agricultural production records, the monitoring of production steps, and the monitoring of products. However, other varied and remarkable blockchain applications include incentive mechanisms, a circular economy, data privacy, product certification, and reputation systems. This study is the first scoping review in this area, following a formal systematic literature review methodology and answering research questions that have not yet been addressed.

Full text

Citation: Sendros, A.; Drosatos, G.;
Efraimidis, P.S.; Tsirliganis, N.C.
Blockchain Applications in
Agriculture: A Scoping Review. Appl.
Sci. 2022,12, 8061. https://doi.org/
10.3390/app12168061
Academic Editor: Arcangelo
Castiglione
Received: 20 June 2022
Accepted: 8 August 2022
Published: 11 August 2022
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applied
sciences
Review
Blockchain Applications in Agriculture: A Scoping Review
Andreas Sendros 1,2 , George Drosatos 1,* , Pavlos S. Efraimidis 1,2 and Nestor C. Tsirliganis 1
1Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
2
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
*Correspondence: gdrosato@athenarc.gr; Tel.: +30-25410-78787 (ext. 322)
Abstract:
Blockchain is a distributed, immutable ledger technology initially developed to secure
cryptocurrency transactions. Following its revolutionary use in cryptocurrencies, blockchain solutions
are now being proposed to address various problems in different domains, and it is currently one of
the most “disruptive” technologies. This paper presents a scoping review of the scientific literature
for exploring the current research area of blockchain applications in the agricultural sector. The aim is
to identify the service areas of agriculture where blockchain is used, the blockchain technology used,
the data stored in it, its combination with external databases, the reason it is used, and the variety of
agricultural products, as well as the level of maturity of the respective approaches. The study follows
the PRISMA-ScR methodology. The purpose of conducting these scoping reviews is to identify the
evidence in this field and clarify the key concepts. The literature search was conducted in April 2021
using Scopus and Google Scholar, and a systematic selection process identified 104 research articles
for detailed study. Our findings show that in the field, although still in the early stages, with the
majority of the studies in the design phase, several experiments have been conducted, so a significant
percentage of the work is in the implementation or piloting phase. Finally, our research shows that
the use of blockchain in this domain mainly concerns the integrity of agricultural production records,
the monitoring of production steps, and the monitoring of products. However, other varied and
remarkable blockchain applications include incentive mechanisms, a circular economy, data privacy,
product certification, and reputation systems. This study is the first scoping review in this area,
following a formal systematic literature review methodology and answering research questions that
have not yet been addressed.
Keywords: blockchain; distributed ledger technology; agriculture; scoping review; PRISMA-ScR
1. Introduction
At the dawn of the 21st century, the agricultural industry, which is still rapidly growing,
represents a turnover of 3.5 trillion USD [
1
], but it also faces many challenges. The most
important of which is the assurance of safe, nutritious, and sufficient food for everyone,
as defined by the United Nations 2030 Agenda for Sustainable Development [2].
According to product safety regulations, everyone must follow specific standards,
such as the GATT and WTO [
3
,
4
]. However, there is no standard global agricultural
protocol shared among agriculture participants, only regional regulations, which leads
to misunderstandings and increases the risks to consumer safety. The agriculture supply
chain involves many intermediaries, such as farmers, distributors, retailers, and final sellers.
Those parties use private databases and documents to store critical information about
the origin and safety of products, to which only regulators have access, making them
vulnerable to a breach or the loss of data [
5
,
6
]. Therefore, trust between them is an essential
part of reducing the risk to supply chain safety [7].
The importance of all the above becomes apparent if we consider that several hazards
can cause physical, biological, or chemical contamination from production to our plate
throughout the food supply chain. A shocking example of this is the 2006 incident in
the United States where a batch of spinach containing E. Coli was distributed in 26 states
Appl. Sci. 2022,12, 8061. https://doi.org/10.3390/app12168061 https://www.mdpi.com/journal/applsci

Appl. Sci. 2022,12, 8061 2 of 37
and infected 205 people, 3 of whom died [
8
]. The remarkable thing was that it took more
than three weeks to find out where the infection came from, while the consequences on
the market were incalculable. Consequently, traceability in the production line has a
critical role [
6
]. Of equal importance is the monitoring, recording, and control of essential
parameters that cover the entire product’s life cycle [
9
]. In addition, in the last decades,
consumers have begun to be interested in the origin, certification, and quality of products in
terms of the importance and attention given when making market decisions [
10
]. Another
major challenge nowadays is food waste due to the expiration of products. For example,
the European Union discards about one-third of the production, which is equivalent to
88 million tonnes [
11
]. Finally, an open-ended issue over time is the equal pay for producers
and the fair trade of products [12].
In short, in agriculture, there are still open issues regarding product traceability
and monitoring, trust among supply chain parties, equal pay for producers, production
sustainability, and other issues. Thus, blockchain could be a potential technology that can
treat most of these issues, with an emphasis on providing greater security to existing or
new solutions in this direction.
1.1. Blockchain Technology
According to Manyika
[13]
, the agricultural sector is one of the industries with the
least integration of digital technologies. Nevertheless, the use of information and commu-
nications technologies, such as blockchain, deep learning, and the Internet of Things (IoT),
promise to bring digitization to agriculture and solve the above problems [
14
]. Specifically,
blockchain, due to its decentralized nature and management, could potentially provide
solutions to ensure the integrity and immutability of transactions.
The first distributed blockchain technology was described as a fundamental compo-
nent of the Bitcoin cryptocurrency [
15
]. This idea proved to be a success and managed
to change the model of central management. The inherent features of blockchain archi-
tecture and design provide properties such as transparency, decentralisation, accessibility,
autonomy, and immutability. In its original application in Bitcoin, the blockchain allowed
users with only a predefined function, which was to exchange cryptocurrencies. How-
ever, that changed drastically in 2015 with the creation of Ethereum [
16
]. Ethereum is
a blockchain system that allows anyone to build applications that will run on it. These
decentralized applications are called DApps, based on smart contracts and written in
high-level programming languages.
Through smart contracts, decentralized structures can enable transactions between
organizations without a central authority to be in control. Blockchain introduces the idea of
“Decentralized Autonomous Organizations” (DAOs), where organizations can form entities
where no central authority will be needed, but everything will be regulated by specific
rules that they will have agreed upon and will be imposed by smart contracts [17,18].
The proper functioning of these DAO entities as well as the blockchain is based on
consensus algorithms. The consensus protocol defines how different nodes agree on a
result that will be appended to the next block [
19
]. It ensures security, data accuracy, and
that all members follow the rules that have been set. The consensus algorithm differs,
depending on the type of blockchain. Public and private blockchain use different consensus
algorithms based on their requirements. The different need for trust between these types
of networks defines the consensus algorithm. In public blockchains where anyone can
participate, there is no trust in the network and therefore heavier consensus algorithms
are needed to ensure network integrity. Consequently, variants of Proof of Work (PoW) or
Proof of Stake (PoS) algorithms are used that offer fault tolerance and security but a slow
transaction confirmation rate [
20
]. On the contrary, in private blockchains, all participants
have a known identity and role, so they are governed by trust and more efficient consent
algorithms can be used in terms of transaction speed. The most common of these are: the
Practical Byzantine Fault-Tolerance (PBFT) and Raft consensus algorithms [
21
]. Finally,
the need for blockchain in particular application domains has led to the recent trend of

Appl. Sci. 2022,12, 8061 3 of 37
creating application-specific consensus algorithms suitable for specific tasks (e.g., IoT,
supply chain, and trading) [20].
1.2. Blockchain Technology in Agriculture
Following this revolutionary idea, the prospects of blockchain evolved rapidly, with block-
chain being used in areas other than cryptocurrencies and smart contracts [
22
] playing a
central role and creating enormous potential. Blockchain can increase transparency and
accountability in supply chain networks and help detect counterfeit products easily, reduce
intermediaries, and facilitate product traceability [
23
]. Such characteristics could potentially
benefit the agricultural sector. Indeed, many of the blockchain advantages we mentioned
are already provided in existing conventional solutions and often increase efficiency. How-
ever, blockchain is an infrastructure that can additionally offer data immutability as well
through its inherent features, to help build confidence between untrusted parties [24].
This trust is essential given the nature of the supply chain, and the confidence that is
achieved between organizations can increase the use of digital technologies [
25
]. The agri-
culture supply chain consists of many different parties (e.g., farmers and resellers) that
are usually not located in the same geographical area and deal with natural products or
services without knowing all the other partners. This complexity of the supply chain can
be problematic and an obstacle to cooperation between the parties [
26
]. Blockchain can
offer a possible solution to this by improving the level of trust between the participants
of the supply chain [
27
,
28
]. Moreover, through the blockchain, there can be transparency
throughout the agricultural chain, which will help build trust indirectly [29].
Additionally, the Food and Agriculture Organization (FAO) of the United Nations has
recognized the importance of the blockchain in the agricultural sector [
30
]. Because of these
potential advantages, companies have already proposed blockchain-based solutions [
31
].
These blockchain applications in agriculture can provide various solutions, such as:
•
Product traceability and logging (e.g., IBM Food Trust [
32
], Ambrosus (https://
ambrosus.io, accessed on 15 June 2022), and TE-FOOD (https://te-food.com, ac-
cessed on 15 June 2022)): Consumers and regulators can ensure the origin of the
products. Moreover, they can store product information from IoT devices and sensors.
•
Ensuring trust between participants (e.g., TrustChain [
28
]): Blockchain can help supply
chain participants trust each other through the transparency and immutability it
can offer.
•
Providing equal pay to producers (e.g., FairChain (https://fairchain.org, accessed on
15 June 2022)): Blockchain can be used to reduce intermediaries and distribute profits
transparently to producers.
•
Product insurance and claiming compensation (e.g., Etherisc (https://etherisc.com,
accessed on 15 June 2022)): Smart contracts can replace insurance documents and
schedule insurance activation according to IoT sensors. All the transactions are
transparent and visible to other parties.
Even though various aspects of blockchain use in agricultural production have been
clarified, some issues still remain open. For the full adoption of blockchain in the agricul-
tural sector, a number of technological barriers must first be addressed, such as blockchain
scalability [
33
] and the cost and performance of blockchain data stores [
34
], as well as
privacy issues related to blockchain usage [
35
]. As the agricultural sector includes many
different autonomous parties, another issue is the management of multi-blockchains [
36
]
for the interoperability between organizations. Finally, some aspects of blockchain use in
the agricultural sector have not been identified in detail, such as which agricultural service
areas the blockchain can be used for, what is the reason for its use, and what type of data
are stored in-chain and off-chain.
The focus of our study is to analyze such aspects of blockchain technology in the
agricultural sector as presented in the scientific literature. Through the scoping review that
we conducted, we try to answer a variety of research questions about the use of blockchain

Appl. Sci. 2022,12, 8061 4 of 37
in agriculture and to identify existing knowledge gaps. Furthermore, this work may lead
to more detailed and systematic reviews of these technologies in agricultural sub-sectors.
1.3. Related Work
Although there are various reviews regarding the use of blockchain in agriculture,
only one is the most extensive, Kamilaris et al.
[37]
, including 49 papers. The same author,
in 2021 [
38
], also published a book chapter which, at that time, had 80 related papers.
The rest of the reviews, e.g., [
39
–
45
], include a smaller number of papers, either due to more
specific questions or mainly due to the early stage of the technology when conducting their
research. This is mainly due to the recent explosion of blockchain use and the corresponding
increase in the related scientific literature. There is also a difference in the exact focus area
of each review paper, with some reviews answering questions about blockchain use in
food [40],
others about blockchain in agriculture and the food supply chain [
37
], or just
about blockchain in agriculture [42,44,45].
The present work, compared to previous related works, is novel in several aspects.
First, our research is the most comprehensive literature review that has been conducted so
far, including a total of 104 research papers. Second, our research area is focused not only
on blockchain but also on distributed ledgers technologies in agriculture. Third, we apply
the widely used PRISMA-ScR [
46
] methodology for systematic scoping reviews. To the
best of our knowledge, this is the first systematic literature search in this field following
a formal methodology. Finally, in our research, we examine a wider range of research
questions, some of which have not been mentioned in the past in the existing literature.
For example, we answer questions about application development beyond the blockchain
technology used, the service area, the maturity level, the country, or the product to which
the application refers. Other such questions were if the data were stored on-chain/off-chain
(and also the off-chain technology used), the reason for using the blockchain, and the type
of blockchain.
1.4. Contribution
Our main contributions can be summarized as follows:
•
We provide a comprehensive scoping review of blockchain applications in agriculture.
•
We answer research questions that have not been addressed in previous work, such
as if the data were on-chain/off-chain, the off-chain technologies used, the type of
blockchain, and the reason for using blockchain. In addition, we set research questions
about the exact blockchain technology, the maturity level, the provided service area,
the agricultural products, and the country.
•
We use a formal methodology as defined by Prisma-ScR. This scoping review is the
first in this multidisciplinary field to the best of our knowledge. This type of review
is the most appropriate knowledge synthesis approach for systematically mapping
concepts that support a broad research area, such as the blockchain in agriculture.
•
We analyze our findings based on nine research questions, visualize the results,
and provide a focused discussion for each research question, as defined by our scoping
review methodology.
1.5. Outline
The remainder of the paper is organized as follows: Section 2describes our research
questions, defines the protocol and the method that we use, and also explains the details of
the features we gather. Section 3presents and visualizes the results of our scoping review.
Section 4summarizes our main findings and discusses our evidence and the limitations of
our study. Finally, Section 5concludes this scoping review paper.

Appl. Sci. 2022,12, 8061 5 of 37
2. Methods
2.1. Goal and Research Questions
Our scoping review is conducted to map the research systematically conducted in this
area and to identify any existing gaps in knowledge to which the scientific community can
contribute. As a result, the following research questions are formulated:
RQ1.
What service areas have been addressed in the current use of blockchain technology
in agriculture?
RQ2. What is the maturity level of blockchain applications in the agricultural sector?
RQ3. Which products are primarily used in agricultural blockchain applications?
RQ4. For which country were the solutions created or implemented?
RQ5. What kind of blockchain technology is used?
RQ6. What type of blockchain is used?
RQ7. What types of data are stored in blockchain in the agricultural applications?
RQ8. Are there data stored off-chain and linked to the blockchain?
RQ9.
What are the main reasons for using blockchain technology in the
agricultural sector?
2.2. Research Protocol
The present study follows the scoping review methodology, the most appropriate
knowledge synthesis approach for systematically mapping concepts that support a wide
research area and categorizing this knowledge. In contrast to the systematic
review [47,48],
research questions do not focus on specific parameters. They also do not define quality
filters, as is the case in a systematic review, which is not easy to happen in an interdisci-
plinary field such as the one we are studying. These make the scoping review suitable in
areas where more specializations coexist, as in our case. The composition of the data is
also grouped, and we do not refer to each one individually, something that would not be
possible with the volume of research we have. Grouping also helps categorize the findings
that we need to draw general conclusions and find gaps in the literature, which is the
purpose of the scoping review.
The scoping review protocol of this study was developed using the PRISMA methodol-
ogy [
49
] and, in particular, the PRISMA-ScR, which is an extension for scoping
reviews [46].
PRISMA methodology is the most used and cited framework for systematic reviews and
meta-analyses, and its extension is used to synthesize data and evaluate the scope of
the literature. A summary of the protocol procedure is demonstrated step by step in the
following subsections.
2.3. Eligibility Criteria
Prerequisites for including the papers in the review were the reference to certain
aspects of blockchain technology that applied to a problem in the agricultural sector.
The papers must be peer-reviewed journal articles or conference papers, published up to
the day the queries were searched, written in English, and refer to our question: the use of
blockchain in agricultural production or related derivatives. Papers were excluded if they
did not fit into the study’s conceptual framework, especially if they were mentioned in
reviews and position papers or blockchain applications not directly related to agriculture.
Moreover, the papers are excluded if they do not have a scientific background, are demo,
or are published without peer review. In addition, we have not used gray literature. Finally,
we have excluded papers that refer to blockchain but do not explicitly mention its use,
e.g., a machine learning system that claims to receive agricultural information from the
blockchain without stating its structure or the data stored on it.
2.4. Information Sources and Search
To identify potentially relevant publications, the following online bibliographic data-
bases were searched: Scopus and Google Scholar. The Scopus database was used because it
contains the most important digital libraries, such as Elsevier,Springer,ACM, and IEEE. It

Appl. Sci. 2022,12, 8061 6 of 37
also provides advanced search and is easy to export. The following query was performed
on 9 April 2021 in Scopus:
TITLE-ABS-KEY ((agriculture OR agricultural) AND (blockchain OR
“distributed ledger”))
Google Scholar was also used, in addition, so that we do not omit significant papers
from the blockchain application in agriculture. The searches were performed on 14 April
2021, and the following queries were used:
allintitle: agriculture blockchain
allintitle: agriculture “Distributed Ledger”
allintitle: agricultural blockchain
allintitle: agricultural “Distributed Ledger”
Results from Scopus were retrieved using the provided export function in BibTeX
format. In Google Scholar, we used the Publish and Perish tool to search for and retrieve
articles in the same format. The BibTeX files were then converted to CSV using the open-
source bibliography reference manager Zotero [
50
]. The citation details for all retrieved
papers were eventually compiled into a single Microsoft Excel file for further study.
2.5. Selection of Sources
In order to achieve the best coherence among the reviewers, we defined the data
we needed to find an answer based on the research questions we asked and created the
appropriate framework for extracting this data from the papers, so that there is a unified
approach. We also set the exclusion criteria as they refer to the eligibility criteria section.
First, we separated the duplicate papers that appeared. Then, the authors of this paper
independently examined the title and abstract of all publications and excluded publications
according to the criteria set. All papers that did not contain an abstract in English, were not
scientific, or just discussion papers were excluded. We also excluded the review papers and
kept them for further analysis in order to compare them with our results. Instead, in this
step, we included papers for further study if any of the above could not be understood from
the title and abstract. The reviewers discussed the papers which they excluded and agreed
on a consolidated list of publications. The four reviewers then independently reviewed
the full text of all retained publications. Everyone extracted the data we set. After this
step, we resolved the disagreements over the data we extracted. If there was no consensus,
discussions were held with other reviewers.
2.6. Data Charting and Data Items
A data charting form was developed jointly by the authors to determine which vari-
ables to export. Then, they independently charted the data and discussed the results. Minor
discrepancies were resolved again by discussion and a unified data chart was constructed
(available upon request).
For each paper included in the list after the first screening, the following data items
were exported:
•Year of publication: as stated in the search engines export results.
•
Source type: publication types which we categorized into (a) conference papers,
(b) journal articles.
•Publisher: as stated in the search engines export results.
For each research paper that was finally included in the scoping review, additional
data items were extracted in order to categorize the paper. The authors studied the papers
to extract mapping keywords related to the scoping review research questions. During this
process, we constructed a classification scheme based on the identified data items. The pa-
pers were classified into the specified categories. Finally, the following additional data
items were exported:

Appl. Sci. 2022,12, 8061 7 of 37
•
Service area: the specific service area considered in the publication, e.g., monitoring,
management, certification, etc.
•
Maturity level, using the following scale: (a) Conceptual: a proposal idea with a specific
system architecture. (b) Simulation: an application of blockchain was created using a
simulated software or framework. (c) Partial experimental: partial experiments have
been performed but not on the blockchain. (d) Experimental: extensive experiments
were performed without creating a complete system with front-end, usually to find
cost and time, but also other aspects of the blockchain. (e) Proof of concept: a proof-of-
concept (POC) approach tests whether a particular concept is feasible from a technical
point of view. The POC approach requires a simple end goal and demonstrates
whether that goal can be achieved or not. It usually has a front-end. (f) Evaluation:
system testing and evaluation with real or not data. (g) Prototype: an initial small-
scale implementation that is used to prove the viability of a project idea. A prototype
attempts to test the critical aspects of the entire system. (h) Piloting: a pilot test
validates a fully functional product that is offered to a portion of your target users. It
has a complete ready-make system and is tested for a subset of our audience.
•
Agriculture product: information about the agricultural products or goods in which the
blockchain application is used.
•
Country: the country, if mentioned, for which the application was created (to solve
specific difficulties that prevailed) or where it was used and evaluated.
•
Blockchain technology: the specific blockchain infrastructure (if any) used or proposed
in a provided solution, e.g., Ethereum, Hyperledger Fabric, etc.
•
Blockchain type: the classic categorization into public, private, and consortium block-
chain or even the NIST categorization [
51
] into permissioned and permissionless
blockchain leads to the problem that it is not clear whether they refer to data reading
or the consensus mechanism. The solution to this problem is the dual name proposed
by the European Commission [
52
] and we follow it in this scoping review. More specif-
ically, this categorization is as follows: (a) public permissionless: in this case, both
the transaction data and the participation in the consensus algorithm are accessible to
all those who participate in the network (such as Ethereum and Bitcoin);
(b) public
permissioned: unlike public permissionless blockchains, while the transaction data
are open to everyone, the transaction validation involves specific users who have
been authorized (such as Ripple and private versions of Ethereum); (c) private per-
missioned: such blockchain networks restrict to specific users both access to data
and participation in the consensus mechanism (such as Hyperledger Fabric); and
(d) private
permissionless: these blockchain networks are not widely known. While
the data are accessible only to authorized users, the consensus mechanism is made by
all participants in the network.
•
Data on blockchain: the specific data stored in the blockchain according to the publications.
•
Off-chain data: the data stored outside the blockchain using other technological solu-
tions. We also mention, in addition to the data, the specific technology (if any) used,
such as IPFS, Swarm, SQL databases, etc.
•
Reason for using blockchain: it describes to what end blockchain technology is exploited
in each solution, such as immutable logging, integrity, transparency, access control, etc.
Furthermore, it practically describes the security problem that blockchain can solve in
each application.
2.7. Synthesis of Results
After the first screening, we analyzed the overall results to present an overview of
the existing literature on blockchain applications in the agricultural sector. We focused
on literature presenting demographic data of the solutions (year, source type, service
area, maturity level, agriculture product, and country) and the data related to blockchain
(blockchain technology, blockchain type, data on blockchain, off-chain data, and reason
for using blockchain). The individual characteristics of each publication are presented in

Appl. Sci. 2022,12, 8061 8 of 37
tabular form. We tried to group the data items as much as possible. We have also computed
and analyzed, in various diagrams, the results of the scoping review. Finally, we summarize
and discuss the finding for each of our research questions.
3. Results
3.1. Selection of Evidence Sources
A total of 636 abstracts were retrieved (398 from Scopus and 238 from Google Scholar).
First, we removed 118 duplicate records. After the first screening, 387 of the remaining
518 papers
were excluded: 110 were not related to our research scope, 30 were not in
English, 44 were introductory materials from conference proceedings, 48 were not scientific
papers, 55 were review papers, 70 were discussion papers, and 35 were papers that could
not be accessed. Eventually, we came up with 131 unique papers identified for the complete
paper analysis. During the second screening, 22 papers were excluded as they were not
relevant to blockchain applications in the agriculture sector. The remaining 104 research
papers were included in the scoping review. The source selection process is shown in
Figure 1. Overall, 16.35% of the retrieved papers were relevant to the study’s topic and
were included in this scoping review.
Records identified through Scopus
(n=398)
Records identified through Scholar
(n=238)
Records identified in total
(n=636)
Full-text papers assessed for eligibility
(n=126)
Records excluded
(n=387)
•110 not related
•30 not in English
•44 introductory
materials
•48 not scientific
•55 review
•35 can’t be
accessed
•70 discussion
paper
Papers included in scoping review
(n=104)
Full-text papers
excluded as not related
to agriculture domain
(n=22)
IdentificationScreening
Records screened
(n=518)
Records removed as
duplicates
(n=118)
Included
Figure 1. Source selection process from bibliographic search engines.
Figure 2shows the yearly distribution of the publications that were retrieved by search
engines (after duplicate removal) and the final papers included in our scoping review.
As we found out, there is an increasing trend in blockchain research in the agricultural
sector. All the papers in our review were published from 2017 onward: 3 papers (3%)
published in 2017, 7 papers (7%) published in 2018, 28 papers (27%) published in 2019,
47 papers (45%) published in 2020, and 19 papers (18%) published until April of 2021.

Appl. Sci. 2022,12, 8061 9 of 37
037
28
47
19
519
62
129
220
82
0
50
100
150
200
250
2016 2017 2018 2019 2020 2021
Number of papers
Year
Finally included papers Retrieved papers
Figure 2.
Yearly distribution of papers retrieved (blue) and finally included (orange) in our scop-
ing review.
Figure 3presents the number of papers per publisher that were finally included in our
scoping review. The IEEE holds the highest number of papers, corresponding to the 42%
(44 papers) of all the relevant papers. Other publishers that appear very often in our papers
collection are Elsevier 13% (13 papers), Spring 13% (13 papers), and MDPI 11% (11 papers).
In addition, few works have been published in IOP 5% (5 papers) and ACM 4% (4 papers).
Finally, there are 14 papers (13%) that have been published in other publishers.
44 (42.4%)
13 (12.5%)
13 (12.5%)
11 (10.6%)
5 (4.8%)
4 (3.8%)
14 (13.4%)
0 5 10 15 20 25 30 35 40 45
IEEE
Elsevier
Springer
MDPI
IOP
ACM
Others
Number of papers
Publisher
Figure 3.
Distribution of papers per publisher related to blockchain applications in the
agriculture domain.
A further analysis of 104 papers related to blockchain application in the agriculture
domain shows that 63 (61%) publications are full conference papers and 41 (39%) are
journal papers (Figure 4). Journal papers are scattered in 25 different journals; only 8 jour-
nal titles have published more than one paper on blockchain applications in agriculture.
The journals with the most published papers are: IEEE Access (6 papers), Sustainability (5
papers), and Computers and Electronics in Agriculture (3 papers). There are also five more
journals with more than one paper in our review: Journal of Cleaner Production,Future
Generation Computer Systems,Sensors,Journal of Computers, and International Journal of Ad-
vanced Computer Science and Applications. Conference papers are published in
59 different
conference proceedings; only four conference proceedings titles published more than one
paper included in this scoping review, namely IEEE ICBC, IEEE ICCCSP, ITIA, and IEEE
ISPA/BDCloud/SocialCom/SustainCom (2 papers).

Appl. Sci. 2022,12, 8061 10 of 37
Conference
paper, 63
(61%)
Journal
papers, 41
(39%)
Figure 4. Number of papers from different types of publication.
3.2. Characteristics of Sources and Synthesis of Results
The characteristics and data chart for each of the 104 research papers included in the
scoping review are presented in Table 1.
The service areas (RQ1) addressed in our findings on the current use of blockchain
technology in agriculture are shown in Figure 5. The majority of papers address the
application of blockchain technology for management purposes (75%) and the monitoring
of products (55%), which, as we expected, are the most common uses. The next favorite
provided service is the certification of products (8%). Other service areas that are addressed
include access control devices (4%), producers’ reputation (4%), products’ trading (4%),
auctions (3%), reward systems (3%), and for data sharing reasons (2%).
78 (75%)
57 (55%)
8 (8%)
4 (4%)
4 (4%)
4 (4%)
3 (3%)
3 (3%)
2 (2%)
010 20 30 40 50 60 70 80
Management
Monitoring
Certification
Access control
Reputation
Trading
Auction
Reward system
Data sharing
Number of papers
Provided service area
Figure 5. Service areas addressed in the papers included in our scoping review.
Overall, blockchain applications in the agricultural sector are at a relatively early stage
of maturity (RQ2), as we found. More than half of the papers propose a solution that has
not been implemented yet, as shown in Figure 6. Most of the research works (39%) are at a
conceptual level, 7% are simulations, and 9% are partially experimental. On the contrary,
19 papers (18%) at the experimental level perform various experiments in blockchain
technology, and 14 papers (13%) are at the proof-of-concept level. Only
14 research
papers
are at a high level of maturity, 7 of them (7%) are at the evaluation level using existing
datasets to test their proposals, 3 papers (3%) have created a prototype implementation,
and finally, 4 solutions (4%) are in a pilot phase.
Figure 7depicts the classification of the research papers by the agricultural sector and
products (RQ3) primarily used in agricultural blockchain applications. Most papers (39%)
refer to the farming sector, while a significantly smaller percentage refers to the livestock
sector (14%). Our findings show that many research solutions (38%) do not explicitly

Appl. Sci. 2022,12, 8061 11 of 37
mention either the product or a specific sector. There is also a small percentage (9%) that
refers to essential goods for the agricultural process. Furthermore, the products on which
the solutions are focused are quite different from each other. The most common are crops
(15%) and organic foods (4%), followed by grain, beef, and milk with 3%. There are also
solutions that belong to the 2% and refer to corn, soybean, oil, wine, chickens, and cows.
Other products that only referred to one solution are: citrus, tea, pumpkin, etc. Finally, we
observe that there are solutions that do not refer directly to the agricultural sector but do so
indirectly, such as water irrigation (6%), photovoltaics (2%), and wastes (1%).
41 (39%)
7 (7%)
9 (9%)
19 (18%)
14 (13%)
7 (7%)
3 (3%)
4 (4%)
0 5 10 15 20 25 30 35 40
Conceptual
Simulation
Partial Experimental
Experimental
Proof of Concept
Evaluation
Prototype
Piloting
Number of papers
Maturity level
Figure 6. Maturity of the research presented in the papers included in our scoping review.
Figure 7. Agriculture sector and products addressed in the papers included in our scoping review.
Figure 8illustrates the geographical location for which the proposed solutions were
created or implemented. Only 36% of the total solutions have been made for a specific
geographical area. More than half of the proposed solutions referred to the Asian continent.
More precisely, 11 papers (30%) focus on China, 5 papers (14%) on India, and 2 papers (5%)
on Vietnam and Pakistan. The remaining 17 blockchain solutions in the agricultural sector
(46%) are scattered in 16 countries; only Spain is referred to with more than one solution,
specifically in 2 papers (5%).
The analysis of each source identified more specific attributes about the blockchain
technology framework (if any) used, the blockchain type utilized in the applications,
the specific data stored on-chain and off-chain, and the reasons for using blockchain; a
summary of the data charted is shown in Table 2.

Appl. Sci. 2022,12, 8061 12 of 37
#papers
1
2
5
11
0
1
2
5
11
#papers
#papers
1
2
5
11
Figure 8.
Countries for which the proposed solutions included in our scoping review were created
or implemented.
Figure 9shows the various blockchain technology frameworks (RQ5) that are con-
sidered by the proposed solutions. The most used blockchain technologies are Ethereum
(35%) and Hyperledger Fabric (20%). On the other hand, a large part of the solutions does
not mention any specific blockchain technology (32%), while some have created a custom
blockchain network (9%) which is either based on an existing framework (e.g., Ethereum)
or not. Other blockchain frameworks used include Hyperledger Sawtooth, IOTA, NEO,
Corda, and Multichain. There are also solutions that incorporate other blockchain-based
technologies, such as BigchainDB and Polkadot.
36 (35%)
21 (20%)
9 (9%)
4 (4%)
3 (3%)
1 (1%)
1 (1%)
1 (1%)
1 (1%)
1 (1%)
33 (32%)
0 5 10 15 20 25 30 35 40
Ethereum
Hyperledger Fabric
Custom
BigchainDB
Hyperledger Sawtooth
IOTA
NEO
Corda
Polkadot
Multichain
Not mentioned
Number of papers
Blockchain framework
Figure 9.
Blockchain technology frameworks considered in the papers included in our scoping review.
Regarding the types of blockchain (RQ6) utilized in the identified solutions, most
of them (36%) use private permissioned blockchains, as shown in Figure 10. Moreover,
a significant percentage of the papers (28%) use public permissionless blockchains, while
5% of the solutions use public permissioned blockchains. Most blockchain frameworks
have a specific blockchain type; however, even if Ethereum is public permissionless by
default, it is also used as a private permissioned or public permissioned. As we can see, no
approach uses private permissioned blockchains, which are rarely used anyway. Finally,
five research papers
(5%) combine
two different types, three papers (3%) combine public
permissioned and private permissioned blockchain networks, and two papers (2%) use
jointly public permissionless and private permissioned. Finally, in 28 research papers (27%),
the type of blockchain is not mentioned.

Appl. Sci. 2022,12, 8061 13 of 37
37 (36%)
29 (28%)
5 (5%)
3 (3%)
2 (2%)
28 (27%)
0 5 10 15 20 25 30 35 40
Private Permissioned
Public Permissionless
Public Permissioned
Public Permissioned,
Private Permissioned
Public Permissionless,
Private Permissioned
Not mentioned
Number of papers
Blockchain type
Figure 10. Types of blockchain presented in the papers included in our scoping review.
The analysis of each source identified the data stored in the blockchain in agricultural
applications (RQ7). These data vary, depending on the solution proposed by each paper,
as shown in Figure 11. The most common type of stored data, as reported in 43 papers
(41%) (Figure 11a), are data from IoT devices, such as temperature, humidity, etc. It should
be noted that many sources do not constantly upload data to the blockchain but only do
so when there are anomalies/invasions, something that occurs in six papers. In contrast,
in another
six solutions,
the data are stored in the blockchain periodically during the day.
The aggregate data are usually stored in external databases. It is also common in the
identified applications to ask users to provide information about products (29%), farmers
(4%), farmland records (3%), seeds (3%), animals (2%), machinery (2%), and ERP data (2%).
Additionally, it is common practice to store data outside the blockchain, and its integrity is
ensured by storing the hashes of this data in the blockchain. More specifically, we identified
8 papers (8%) that store IPFS files’ hashes and 12 papers (12%) that store the hashes of data
stored in external databases. It is essential to mention that, in recent works, the storage of
tokens (4%) in blockchain has begun, while there are proposals for the storage of public
keys of IoT devices (2%). In addition to all the above data stored in the blockchain, 34 other
different data types have been reported in a single solution. Figure 11b shows a word
cloud of all the different data types considered in the agricultural blockchain applications
included in the scoping review for a visual overview.
The data stored off-chain (RQ8) in the identified solutions are shown in Figure 12.
From the 104 research solutions proposed, 37 of them (36%) mention at least one external
storage. Most of the data stored off-chain are IoT data (13%) and product information (12%).
Media files (6%), hashes (3%), RFID data (2%), and private data (2%) are also stored off-
chain. Less common to be stored externally in research papers are reputation scores (2%),
transaction logs (1%), GIS data (1%), and credentials (1%). In addition to the data stored
externally, we present the storage technology used. No specific technology is mentioned in
most of the external data storage solutions (13%). The most common technology used in
conjunction with blockchain is IPFS (13%). Moreover, other distributed storage systems are
BigchainDB (2%), QLDB (1%), OurSQL (1%), and a not-specific distributed database (1%).
Finally, there are solutions that use the SQL (4%) and NoSQL (3%, including MongoDB)
databases, as well as cloud storage (2%) and BigQuery (1%).
Each agriculture application uses blockchain technology for different reasons (RQ9)
in order to offer specific advantages in the field of data security, as shown in Figure 13.
For example, most papers in agriculture use blockchain for product immutable logging
(72%) which prevents data tampering. Moreover, it is commonly used to achieve trans-
parency (61%) and integrity (50%), the latter especially enhancing system security against
malicious attacks. An additional reason for using the blockchain is the traceability (18%)
of products at any time, which is a prerequisite for food safety. Other reasons include
ensuring access control to devices or users (6%), scheduling (5%), storage immutable assets
(4%), data availability (3%), and finally, for incentives (1%). All of the reasons for using
blockchain solve various cyber threats and product security issues.

Appl. Sci. 2022,12, 8061 14 of 37
43 (41%)
30 (29%)
12 (12%)
8 (8%)
4 (4%)
4 (4%)
3 (3%)
3 (3%)
2 (2%)
2 (2%)
2 (2%)
2 (2%)
34 (33%)
0 5 10 15 20 25 30 35 40 45
IoT data
Product information
Data hash
IPFS hash
Tokens
Farmers information
Farmland records
Seed information
ERP data
Animal information
Machinery information
IoT public key
Other
Number of papers
Data on blockchain
(a)
Product information
IoT data
Farmers information
Organic food inspection agency results
Farmland records
Seed information
Encrypted product information
Encrypted product private data
Farmer yield commitment
Hash of previous product
Data hash
IPFS hash
Machinery information
Production process
Trading information
Drone operation
Reputation score
Authentication information
TokensAccess policy
IoT public key
Job description
Policy headers
Product ratings
Scheduling data
Transactions information
Fertilizer information
Insurance information
Parent transaction hash
Animal information
Parties information
RFID Access control
Genes information
Wages
Access records
IoT devices
Transaction
Block hash
Drone data
ERP data
GIS data
Pre-orders
Resources
SDN rules
Contract
Tasks
(b)
Figure 11.
Types of data stored in the blockchain in the papers included in our scoping review.
(a) Popular data types stored in blockchain, (b) a word cloud of the total data stored in blockchain.
Figure 12.
Types of data stored off-chain and the corresponding storage technology used. The groups
shown in the external ring are overlapping, that is, some items might belong to more than one group.

Appl. Sci. 2022,12, 8061 15 of 37
75 (72%)
63 (61%)
52 (50%)
19 (18%)
6 (6%)
5 (5%)
4 (4%)
3 (3%)
2 (2%)
010 20 30 40 50 60 70 80
Logging
Transparency
Integrity
Traceability
Access control
Scheduling
Assets
Availability
Incentitive
Number of papers
Reason for using blockchain
Figure 13. Reasons for using blockchain exploited in the papers included in our scoping review.
Overall, in this section, we presented the raw data of our analysis (Tables 1and 2)
and visualized the findings of our scoping review for each research question
(Figures 5–13).
An overview of the data charting keywords that were identified from our analysis according
to the research questions (RQ1–RQ9) is presented as a mind map in Figure 14.
Service Area
Management
Monitoring
Auction
Access control
Certification
Data sharing
Reputation
Reward system
Trading
Blockchain
Technology
Maturity Level
Simulation
Conceptual
Partial Experimental
Experimental
Proof of Concept
Evaluation
Prototype
Piloting
Hyperledger Fabric
Ethereum
Custom
Hyperledger Sawtooth
BigchainDB
IOTA
NEO
Corda
Polkadot
Multichain
Blockchain Type
Public Permissionless
Private Permissioned
Public Permissioned
Private Permissionless
Reason for using
Blockchain
Logging
Incentive
Transparency
Integrity
Scheduling
Assets
Traceability
Availability
Access control
Data on Blockchain
Product information
IoT data
IPFS hash
Data hash
Tokens
Farmers information
Animal information
Farmland records
Seed information
Machinery information
ERP data
IoT public key
...
Off-chain Data
Off-chain Technology
SQL
IPFS
Distributed database
QLDB
OurSQL
BigchainDB
Cloud
MongoDB
BigQuery
Private data
Product information
Hash
Credentials
Reputation score
RFID data
GIS data
Transaction Log
Blockchain
applications in the
agriculture domain
Multimedia
IoT data
Figure 14.
The classification scheme that emerged from the analysis of papers included in this scoping
review presented as a mind map.

Appl. Sci. 2022,12, 8061 16 of 37
4. Discussion
4.1. Summary of Evidence
The primary outcome of this scoping review shows that blockchain technology has
so far been proposed to address many issues in several different agricultural applications,
as summarized in the following subsections.
4.1.1. Blockchain Frameworks
One of our primary findings was the variations in the specific blockchain framework
used (RQ5). The result reflects the situation that prevails in the overall ecosystem of the
blockchain. Most solutions use Ethereum and Hyperledger Fabric. Beyond that, a large
percentage do not mention the blockchain technology they use. The papers that do not
mention technology are mainly conceptual (67%, 22 out of 33 papers). Apart from the
above in the use of blockchain in the agricultural sector, other technologies have been used
less frequently, such as Hyperledger Sawtooth (3%), IOTA (1%), NEO (1%), Corda (1%),
and Multichain (1%). Two assistive blockchain-based technologies, BigchainDB (4%) and
Polkadot (1%), also appear. Finally, seven research papers in this field combine more than
one technology. All these papers use Ethereum combined with another
technology [53–58],
except one [
59
]. In one of them [
58
], Polkadot is used for two-chain communication, which
allows cross-blockchain transfers. Correspondingly, we can say that according to the
purpose of each application, the appropriate blockchain technology has been chosen. Each
blockchain framework has a different ecosystem and philosophy that is reflected through
its blockchain type (RQ6). The only blockchain we have come across in our study that can
be adapted and used in more than one blockchain type is Ethereum which can be either
public permissionless, public permissioned, or private permissioned. Finally, we concluded
that most solutions opt for private permissioned blockchains, possibly trying to represent
some existing systems.
4.1.2. Data On-Chain and Off-Chain
Blockchain technologies show the ability to handle various security issues. An impor-
tant aspect in this direction was the identification of data stored on-chain and off-chain
(RQ7 and RQ8). Based on the results of this scoping review, blockchain technology has been
proposed more frequently for storing data by sensors and IoT devices in order to monitor
specific aspects of the production process. This appears in 43 research papers (41%). These
data are mainly used to monitor the process and, secondarily, used to manage information
or the product. In addition to the above, there are four solutions that store access control
and authentication policies, for IoT devices, on-chain [
60
–
62
] but also off-chain [
63
]. Some
research papers suggest the periodical storage of data in the blockchain [
55
,
64
–
68
], so as not
to unnecessarily burden the additional cost of storing and using the blockchain. In some
cases, an external database is usually used to store the aggregate data [
65
,
66
,
68
]. In the
corresponding category of solutions, there are also approaches that store only anomalies
presented in the data from the IoT devices [
65
,
66
,
69
–
71
]. This is usually to ensure the
integrity of the information and not to distort it. Almost all of these solutions that store
only critical data in the blockchain have external storage. Both in the case of periodic
storage and in the case of abnormal storage, all solutions use Ethereum, except one that
uses Hyperledger Fabric. This makes sense because, in Ethereum, storage costs are taken
into account when creating the architecture.
Another common architectural scheme in creating decentralized applications is to
store hashes in the blockchain and actual data in external storage. Most architectures use
IPFS to store agriculture data and the blockchain stores either the IPFS hash [
72
–
79
] or the
data hash [
58
,
59
,
80
]. There is a study [
58
] that stores data from sensors in IPFS, then the
hash of this data is stored in a private permissioned blockchain, while the block hash of this
blockchain and the height of the block are stored in Ethereum. In the latter, an incentive
mechanism is activated, through a smart contract, to reward the user who performed the

Appl. Sci. 2022,12, 8061 17 of 37
mining in the private permissioned blockchain. In this way, the authors achieve the security
that a private permissioned blockchain would not have.
As reflected in our research, a new trend in the blockchain is the digital representation
of real-life assets through a digital twin. A digital twin is a virtual representation of a phys-
ical object or system, usually in multiple stages of its life cycle [
81
].
As Pylianidis et al. [82]
point out, the use of digital twins could bring significant benefits to the agricultural process.
The blockchain has also been proposed to represent digital twins using tokens. The re-
search that has been conducted represents products as tokens (following the ERC20 token
standard) that are indirectly related to the agricultural sector, such as water and energy that
farmers need to share [
54
,
83
]. Their use as a currency for transactions between producers
and buyers has also been suggested [
84
]. They can also be used as a reward system for the
virtuous use of water [
85
], where the smart contract is a digital twin of an IoT device that
monitors water consumption. Due to the lack of infrastructure, the researchers created a
module for IoT devices to communicate directly with the smart contract [
85
,
86
]. According
to our findings, no research has been conducted on the digital representation of the product,
which would help in the certification and traceability of the product from the farm to the
fork [87].
In addition to all the above data, we have an additional 42 different types of data
stored in the blockchain. These may include public keys from IoT data authentication
devices, farmer information, farmland records, job descriptions and contracts to define the
work of some farmers, information for machines that can be rented to farmers, RFID data
or GIS sensors, blockchain access rules, drone data, pre-orders that may be available, and
product ratings, as well as information on seeds, animals, etc. These different types of data
show the multiple solutions that blockchain can potentially offer in the agricultural sector.
4.1.3. Solutions Maturity
Another research question that we explored is the maturity level of the
solutions (RQ2).
One of the main findings was that blockchain applications in the agricultural sector are
at a relatively early stage of maturity. More than half of the works (55%) describe the
architecture, have conducted some simulations, or have been partially experimental (not
in the blockchain). A total of 18% of the papers have conducted experiments to test the
functionalities of the blockchain, such as its connection to IoT devices and cost issues, while
13% have made a proof of concept of the proposed solution. According to our results, only
7% are at the level of evaluation, 3% at the level of prototyping, and 4% at the level of
piloting the solution.
The evaluation of the proposed solutions is achieved using datasets from companies
and IoT devices [
80
,
88
,
89
], data created artificially [
57
,
90
], and real-world data from the
agricultural sector [
59
,
70
]. There are also applications in our findings where evaluation
is limited to laboratory tests or simulations. Based on our results, we only found three
prototype applications [
91
–
93
]. All these prototype applications were published from 2020
onward, showing us that maturity is now growing, and real applications are being created.
In addition, we identified four solutions in a pilot phase that have been installed, tested,
and used in real conditions. The first application [
94
] is a pilot, mainly in Nigeria, with the
aim of renting tractors for agricultural work. In the works proposed by Wang et al.
[79]
and Yang et al.
[95]
, the main focus was on the traceability of products, and they have been
applied in factories in China. These three applications have been created using Hyperledger
Fabric. Finally, another research work [
96
] uses the IOTA Tangle network to record the data
from IoT devices and is in a pilot application in three farms in Greece. Interestingly, no
application that uses Ethereum as blockchain technology is yet at this maturity level.
As the technology matures and more industrial applications emerge, real-world pilot
demonstrations, such as the above, will help shape the field of more mature applications
and reveal the most appropriate blockchain applications in the agricultural sector.

Appl. Sci. 2022,12, 8061 18 of 37
4.1.4. Variety of Agricultural Products and Countries
The use of blockchain does not focus primarily on a specific product (RQ3), as we
found in our research. Instead, there are general terms, such as crops, organic food,
and water, that are mentioned in most studies, but beyond that, there is a dispersion of
31 different products. It is interesting to notice that we have more references to farming
products than to animal products. Although the difference is about three times smaller,
39%, e.g., [
78
,
97
,
98
] vs. 14%, e.g., [
73
,
92
], a significant percentage (38%) of the solutions do
not indicate the industry to be used. A small percentage (9%) refers to goods needed in the
agricultural sector, such as water, energy, and proper waste management. This shows us
that researchers can focus on specific products that would be in line with blockchain logic
but also that there is a need for agnostic solutions in the supply chain.
The systems created for specific countries (RQ4) try to solve problems in the essential
products of each country, but no specific association of an individual product with each
country is shown. Even in China, for which more solutions have been created, mainly
agricultural products are mentioned. Finally, it is worth noting that most current solutions
are proposed for Asian countries.
4.1.5. Reason for Using Blockchain
In this scoping review, we also research the reason for using blockchain in the agri-
culture sector (RQ9). These reasons are mainly to solve the various cyber threats and food
safety issues of the existing IT solutions in agriculture. As a result, most solutions use
blockchain for its inherent characteristics, such as data transparency and integrity. This
happens at 61% and 50%, respectively. Moreover, few papers (3%) [
57
,
72
,
99
] use another
intrinsic feature of blockchain technology: its data availability. It should be mentioned
that many solutions involve the use of blockchain over conventional databases due to
the availability provided but do not clearly define it, so it has not been included in our
respective count. In addition to the above reasons for using blockchain, a typical process
is storing product information and tracking information. This immutable data logging is
used in most research papers (72%), e.g., [
95
,
100
,
101
]. It should be noticed that although
blockchain is used for logging and storing data, such as IoT data (solving the problem
of counterfeiting), it should not be misused. Blockchain in general and especially public
permissionless blockchains should not be used to store the overall data of an application.
Such storage is costly and increases the size of the blockchain, making it non-functional.
Instead, blockchain technology should be used as designed to store critical data to which
the blockchain gives an immutable feature. Another common reason for using blockchain
is traceability, which is found in 18% of the solutions, e.g., [
57
,
102
]. As before, we need
to be aware that some solutions misinterpret that traceability is an inherent feature of
blockchain, which is not entirely accurate. Although the ledger itself provides traceability,
this possibility cannot be easily and efficiently achieved without a specific architecture and
without third-party frameworks [34].
Blockchain has also been used to schedule various processes in the agricultural sector.
Scheduling may involve hiring machinery from farmers for specific tasks [
94
,
103
] or hiring
seasonal workers for agricultural jobs [
104
]. It may also involve priority scheduling for
defined tasks with robot coalitions [
99
] or fixing IoT devices using autonomous drones [
67
].
Based on our findings, blockchain technology has also been used to provide access control
solutions [
60
,
61
,
93
,
105
–
108
]. These solutions store data in the blockchain for access control,
such as the public keys and access policy, for security reasons. Most devices for which
access control is used are IoT devices. Finally, the blockchain has been utilized as an
incentive mechanism for the effective management of waste by farmers [109].
4.1.6. Provided Service Area
Following the research question about the reasons for using blockchain, we examine
which service is provided by the respective applications (RQ1). Based on our findings,
more than half of the applications have been created to provide product monitoring or

Appl. Sci. 2022,12, 8061 19 of 37
management. This is observed in 55% and 75% of the papers, respectively. A unique
feature is that most applications (61%), which have been created for product monitoring,
store data from IoT devices, e.g., [
66
,
105
]. In the case of management, this may relate to
the process by which the product went through the various stages of production as well
as its resale,
e.g., [57,110].
This model of all transaction availability promotes the circular
economy model. Most of the time, management and monitoring are combined in the
proposed solutions. Management can also refer to the coordination of processes, such as
the rental of
equipment [94],
the use of robots [
99
], and the proper distribution of a good
such as water, energy, or waste, e.g., [83,86,109].
Although not so many applications have been created extensively, one industry that
developed mainly after 2019 is product certification (8%). In most cases, the provided
solutions certify the authenticity of the product’s origin [
102
,
111
] and the conditions under
which it was developed [
69
,
98
]. Therefore, the data stored in the blockchain are related
to both the product and the process other than the IoT data. We notice that they refer
more often to organic products and are mainly interested in the transparency and in-
tegrity provided by the blockchain. Furthermore, a research paper uses GIS to prove the
location [111].
A different emerging field of blockchain applications in agriculture is auctions (3%) and
product trading (4%). In the first category, we identified three research papers [
89
,
97
,
112
]
that proposed a system of offers for the sale of agricultural products. All these proposed
solutions belong to the farming sector. Respectively, there are applications that deal with
the trading of either agricultural products [
113
] or energy and water for crops [
54
,
83
].
In addition, there is a proposed solution that exchanges products based on the farmer’s
rating [114].
Other services provided by agricultural blockchain applications are reputation (4%)
and reward systems (3%). The rationale for a reputation system is clear, and such systems
aim to capture product and producer ratings. The reason for using blockchain in such
applications is the integrity that it provides, something that our research thoroughly
verifies [
77
,
78
,
114
,
115
]. Additionally, blockchain is highly associated with reward system
applications. More precisely, we observed that all incentive solutions related to data
management are also indirectly related to blockchain, such as water [
85
,
86
] and
waste [109].
The validation of the incentive mechanism is performed by the IoT data stored in the
blockchain. Finally, one last type of provided service is data sharing, and blockchain is
used for authentication [106] or as an incentive mechanism as mentioned above [56].
4.2. Study Limitations
The limitations of this scoping review are related to the publications’ maturity and the
bibliographic databases included for retrieving publications. Our search looked at some
(not all) of the most popular scientific literature indexing systems. Because our research
was focused on the scientific literature, we did not take into account the gray literature
as well as the real-life implementations, something that can be deduced from the holistic
examination of the problem. Our research scope returned heterogeneous data that were not
easy to classify for conducting the study, even in our case. Finally, as a limitation, it should
be noted that this field is still in its infancy and most works repeat the same structure in
their architecture, while, in some cases, blockchain is used as a panacea.

Appl. Sci. 2022,12, 8061 20 of 37
Table 1. Research papers included in the scoping review, their characteristics, the agriculture products on which they focus, and the country of application.
#Author Year Source Type Service Area Maturity Level Agriculture Product Country
1Abraham and Santosh Kumar [116]2020 Conference Management Conceptual India
2Ahmed et al. [117]2020 Conference Management (fertilize) Conceptual Bangladesh
3Alonso et al. [101]2020 Journal Monitoring, Management (IoT platform) Partial Experimental (not in the blockchain) Milk Spain
4Arena et al. [102]2019 Conference Certification (olive) Experimental Extra virgin oil
5Arshad et al. [60]2020 Conference Monitoring (with Access control) Partial Experimental (not in the blockchain) Pakistan
6Awan et al. [118]2020 Journal Monitoring (IoT with energy efficiency) Simulation (Matlab)
7Awan et al. [72]2020 Conference Monitoring, Management (crop) Simulation (Matlab) Crops, Grains Pakistan
8Bakare et al. [91]2021 Conference Management (subsidies) Prototype India
9Balakrishna Reddy and Ratna Kumar [113]2020 Conference Certification (quality), automate trading Conceptual Organic food India
10 Basnayake and Rajapakse [98]2019 Conference Management, certification (organic food) Proof of Concept Organic food Sri Lanka
11 Bechtsis et al. [119]2019 Conference Monitoring, Management Proof of Concept
12 Benedict et al. [70]2020 Conference Monitoring (rubber manufacture) Evaluation Rubber India
13 Bordel et al. [55]2019 Conference Monitoring, Management (irrigation system) Partial Experimental (not in the blockchain) Water
14 Bore et al. [94]2020 Conference Management (tractor leasing) Piloting Nigeria
15 Branco et al. [120]2019 Conference Monitoring, Management (mushroom) Conceptual Mushrooms
16 Cao et al. [92]2021 Journal Monitoring, Certification (beef) Prototype Beef Australia, China
17 Caro et al. [57]2018 Conference Management (crop) Evaluation
18 Casado-Vara et al. [121]2018 Conference Management Conceptual
19 Chen et al. [122]2021 Journal Management Simulation (Python) Corn (use case)
20 Chinnaiyan and Balachandar [53]2020 Conference Monitoring, Management (IoT, drones) Conceptual
21 Chun-Ting et al. [123]2020 Conference Monitoring Conceptual
22 Cong An et al. [124]2019 Conference Monitoring, Management Proof of Concept
23 Dawaliby et al. [67]2020 Conference Monitoring (farm), Management (drone operations) Proof of Concept
24 Dey et al. [125]2021 Journal Certification (product with QR code) Simulation (Python) Milk, Pumpkin UK
25 Dong et al. [64]2019 Conference Monitoring, Management Conceptual Camellia oil
26 Du et al. [73]2020 Conference Monitoring, Management Partial Experimental (consensus protocol)
27 Enescu et al. [54]2020 Journal Management, Trading (energy) Proof of Concept Photovoltaic, Water Romania
28 Enescu and Manuel Ionescu [84]2020 Conference Monitoring, Management Conceptual
29 Friha et al. [61]2020 Conference Access control, Management (SDN IoT devices) Experimental
30 Hang et al. [105]2020 Journal Monitoring Proof of Concept Fish

Appl. Sci. 2022,12, 8061 21 of 37
Table 1. Cont.
#Author Year Source Type Service Area Maturity Level Agriculture Product Country
31 Hao et al. [74]2018 Journal Monitor, Management, Certification Experimental
32 Harshavardhan Reddy et al. [126]2019 Journal Management (economic efficiency) Conceptual
33 Hong et al. [127]2019 Conference Monitoring, Management Conceptual Chicken (use case)
34 Hu et al. [59]2021 Journal Monitoring, Management (organic food) Evaluation Organic food, Citrus
(use case) China
35 Iqbal and Butt [128]2020 Journal Monitoring (animal invasion), Management (crop) Partial Experimental (not in the blockchain) Crops
36 Iswari et al. [129]2019 Conference Monitoring, Management Conceptual Cocoa Indonesia
37 Jaiswal et al. [97]2019 Conference Management, Auction Experimental Grain
38 Jaiyen et al. [130]2020 Conference Monitoring, Management Proof of Concept
39 Jiang et al. [131]2020 Conference Management Conceptual Chicken (use case)
40 Kawakura and Shibasaki [132]2019 Journal Monitoring (hoe’s movement) Experimental Hoe
41 Khan et al. [88]2020 Journal Monitoring, Management (with deep learning) Evaluation
42 Krasteva et al. [133]2020 Conference Management (genes) Conceptual Genes Bulgaria
43 Kumar et al. [80]2021 Journal Privacy preserving management (UAV) Evaluation
44 Lamtzidis et al. [96]2019 Journal Monitoring, Management Piloting Vineyards (use case) Greece
45 Leme et al. [134]2020 Conference Monitoring Conceptual Cows Brazil
46 Leng et al. [110]2018 Journal Management (supply chain) Simulation (Matlab) China
47 Liao and Xu [135]2019 Conference Monitoring, Management (quality safety) Conceptual Tea
48 Lin et al. [136]2018 Conference Monitoring, Management Conceptual
49 Lin et al. [137]2017 Journal Monitoring (water) Conceptual Water Taiwan
50 Liu et al. [71]2018 Journal Monitoring, Management Experimental
51 Lu et al. [106]2020 Conference Authenticated data sharing system Conceptual Crops
52 Madhu et al. [138]2020 Conference Monitoring, Management (crop) Proof of Concept Crops
53 Mao et al. [89]2018 Journal Management, Auction Evaluation Wheat, Corn, Soybean China
54 Marinello et al. [139]2017 Conference Management Conceptual Meat Italy
55 Meidayanti et al. [140]2019 Conference Monitoring, Management Conceptual Beef
56 Miloudi et al. [69]2020 Conference Management, Certification (crop) Conceptual Crops
57 Murali and Chatrapathy [114]2019 Journal Reputation system, Trading Partial Experimental (not in the blockchain)
58 Nadeem Akram et al. [141]2020 Conference Management (with QR) Conceptual Apple (use case) India
59 Nguyen et al. [142]2020 Conference Management Conceptual Crops Vietnam
60 Nguyen et al. [100]2019 Conference Management (insurance for disasters) Experimental Crops Vietnam

Appl. Sci. 2022,12, 8061 22 of 37
Table 1. Cont.
#Author Year Source Type Service Area Maturity Level Agriculture Product Country
61 Orjuela et al. [90]2021 Journal Monitoring, Management Evaluation Colombia
62 Osmanoglu et al. [115]2020 Journal Management, Reputation system Conceptual Crops
63 Öztürk et al. [143]2021 Conference Monitoring (livestock welfare with machine learning) Conceptual Cows Spain
64 Paul et al. [144]2019 Conference Management (loaning system) Proof of Concept Crops
65 Pincheira et al. [56]2020 Conference Data sharing (incentive mechanism) Conceptual
66 Pincheira et al. [86]2020 Conference Monitoring, Management (water), Reward system Partial Experimental (not in the blockchain) Water
67 Pincheira et al. [85]2021 Journal Monitoring, Management (water), Reward system Experimental Water
68 Pinna and Ibba [104]2019 Conference Management (temporary employing contract) Conceptual
69 Pooja et al. [112]2020 Conference Management, Auction Conceptual Seeds, Crops
70 Pranto et al. [65]2021 Journal Monitoring, Management Experimental
71 Prashar et al. [75]2020 Journal Monitoring, Management Experimental India
72 Raboaca et al. [83]2020 Journal Management, trading (energy) Proof of Concept Photovoltaic, Water
73 Rambim and Awuor [145]2020 Conference Management (milk delivery system) Conceptual Milk Kenya
74 Ren et al. [58]2021 Journal Secure Management (double chain) Experimental
75 Revathy and Sathya Priya [146]2020 Conference Management Conceptual Crops
76 Saji et al. [147]2020 Conference Management Conceptual
77 Salah et al. [76]2019 Journal Monitoring, Management Conceptual Soybean
78 Saurabh and Dey [148]2021 Journal Monitoring, Management Conceptual Wine (use case)
79 Shahid et al. [77]2020 Journal Monitoring, Management, Reputation system Experimental Crops
80 Shahid et al. [78]2020 Conference Monitoring, Management, Reputation system Experimental Crops
81 Shih et al. [111]2019 Journal Certification Experimental Organic Food
82 Shyamala Devi et al. [149]2019 Conference Monitoring Proof of concept
83 Smirnov et al. [99]2020 Conference Management (robot coalition for precision farming) Conceptual Crops
84 Son et al. [68]2021 Journal Monitoring, Management Proof of concept
85 Surasak et al. [150]2019 Journal Monitoring, Management Proof of concept Beef Thailand
86 Tan and Zhang [151]2021 Journal Monitoring (for authenticate loans) Partial Experimental (not in the blockchain)
87 Umamaheswari et al. [152]2019 Conference Management Proof of Concept Crops
88 Vangala et al. [63]2021 Journal Access control (safe IoT), Monitoring Experimental
89 Wang et al. [153]2020 Conference Management (anti-counterfeiting) Conceptual
90 Wang et al. [79]2021 Journal Monitoring, Management Piloting Crops China
91 Wang and Liu [154]2019 Conference Monitoring Conceptual
92 Wu and Tsai [62]2019 Journal Access control (secure system) Partial Experimental (not in the blockchain)

Appl. Sci. 2022,12, 8061 23 of 37
Table 1. Cont.
#Author Year Source Type Service Area Maturity Level Agriculture Product Country
93 Xie et al. [66]2017 Conference Monitoring Experimental
94 Xie and Xiao [155]2021 Conference Monitoring (quality of product) Conceptual China
95 Xie et al. [156]2019 Conference Monitoring, Management Experimental China
96 Yang and Sun [157]2020 Conference Management Conceptual China
97 Yang et al. [103]2020 Journal Management (leasing scheduling system) Simulation
98 Yang et al. [107]2020 Conference Monitoring (livestock) Conceptual
99 Yang et al. [95]2021 Journal Monitoring, Management Piloting Fruit, Vegetables China
100 Yi et al. [158]2020 Conference Management Experimental
101 Yu et al. [159]2020 Conference Monitoring, Management (transaction, quality) Experimental
102 Zhang [109]2019 Conference Management (wastes), Reward system Conceptual Wastes China
103 Zhang et al. [93]2020 Journal Monitoring, Management Prototype Grain China
104 Zhaoliang et al. [108]2021 Journal Monitoring (privacy preserving) Simulation (not in the blockchain)
Table 2. Descriptive data on the particular blockchain application presented in each of the papers included in the scoping review.
#Author Blockchain Technology Blockchain Type Data on Blockchain Off-Chain Data Reason for Using Blockchain
1Abraham and Santosh Kumar [116] Hyperledger Fabric Private Permissioned Farmer information Transparency, Logging
2Ahmed et al. [117] Hyperledger Fabric Private Permissioned Fertilizer information Integrity, Logging
3Alonso et al. [101] IoT data hash IoT data (BigQuery) Integrity, Traceability, Logging
4Arena et al. [102] Hyperledger Fabric Private Permissioned IoT data Integrity, Logging, Traceability
5Arshad et al. [60] Hyperledger Fabric Private Permissioned IoT data, Policy headers,
Access records Access control, Integrity, Logging
6Awan et al. [118] IoT data Integrity, Logging
7Awan et al. [72] IPFS hash Product growth information, Media
files (IPFS) Integrity, Availability
8Bakare et al. [91]Custom Public Permissioned Farmland records Transparency, Logging
9Balakrishna Reddy and
Ratna Kumar [113]Ethereum Public Permissionless Product information Transparency, Logging
10 Basnayake and Rajapakse [98] Ethereum Public Permissionless Production process Transparency, Logging
11 Bechtsis et al. [119] Hyperledger Fabric Private Permissioned Product information Integrity, Logging
12 Benedict et al. [70] Hyperledger Fabric Private Permissioned IoT data (anomalies) Integrity, Logging
13 Bordel et al. [55] Ethereum, - Public Permissioned, Private
Permissioned IoT data (periodically),
Data hash Integrity, Logging
14 Bore et al. [94] Hyperledger Fabric Private Permissioned IoT data, Farmland records,
Machinery information Integrity, Transparency, Scheduling,
Logging

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Table 2. Cont.
#Author Blockchain Technology Blockchain Type Data on Blockchain Off-Chain Data Reason for Using Blockchain
15 Branco et al. [120] Data hash IoT data Integrity
16 Cao et al. [92] Ethereum Public permissionless Product information Transparency, Logging, Traceability
17 Caro et al. [57] Ethereum, Hyperledger Sawtooth Public Permissioned, Private
Permissioned IoT data Transparency, Availability, Logging,
Traceability
18 Casado-Vara et al. [121] Trading information Transparency, Logging
19 Chen et al. [122] Product Information Integrity, Logging
20 Chinnaiyan and Balachandar [53] Ethereum, Multichain Private Permissioned IoT data, Drone data Integrity, Logging
21 Chun-Ting et al. [123] Ethereum Private Permissioned IoT data Integrity
22 Cong An et al. [124] Ethereum Public Permissionless Product information Transparency, Logging, Traceability
23 Dawaliby et al. [67] Ethereum Private Permissioned IoT data (periodically), Drone
operation Integrity, Logging, Scheduling
24 Dey et al. [125]Custom Product information Farm information, Manufacturing
information Transparency, Logging
25 Dong et al. [64]IoT public key, IoT data
(periodically) Integrity
26 Du et al. [73] Hyperledger Fabric Private Permissioned IPFS hash Product information (IPFS), Private
data Integrity
27 Enescu et al. [54] Ethereum, BigchainDB Public Permissionless Tokens (ERC20) Sources information, Personal
information (BigchainDB, SQL) Transparency, Assets
28 Enescu and Manuel Ionescu [84] Ethereum Public Permissionless Tokens (ERC20) Product information (distributed
database) Transparency, Assets
29 Friha et al. [61] Hyperledger Sawtooth Private Permissioned IoT devices, IoT data, SDN
rules Integrity, Access control, Logging
30 Hang et al. [105] Hyperledger Fabric Private permissioned IoT data, Product information,
Access policy Integrity, Logging, Access control
31 Hao et al. [74] Ethereum Public Permissionless IPFS hash IoT data, Media files (IPFS),
Blockchain transaction hash Integrity
32 Harshavardhan Reddy et al. [126] Product information Transparency, Logging
33 Hong et al. [127] Hyperledger Fabric Private Permissioned IoT data, Product information Transparency, Logging, Traceability
34 Hu et al. [59] Custom, BigchainDB Private Permissioned Data hash IoT data (IPFS), Data hash
(BigchainDB) Integrity
35 Iqbal and Butt [128]IoT data (animal invasion),
Product information Transparency, Logging
36 Iswari et al. [129] Product information Transparency, Logging
37 Jaiswal et al. [97] Ethereum Public Permissionless Product information Integrity, Transparency, Logging
38 Jaiyen et al. [130] Hyperledger Fabric Private Permissioned IoT data Transparency, Logging, Traceability
39 Jiang et al. [131] IoT data Transparency, Logging, Traceability

Appl. Sci. 2022,12, 8061 25 of 37
Table 2. Cont.
#Author Blockchain Technology Blockchain Type Data on Blockchain Off-Chain Data Reason for Using Blockchain
40 Kawakura and Shibasaki [132] Corda Private Permissioned IoT data (hoe) Logging
41 Khan et al. [88] Hyperledger Fabric Private Permissioned IoT data Logging, Traceability
42 Krasteva et al. [133] Private Permissioned Genes information Integrity, Logging
43 Kumar et al. [80] Ethereum (custom consensus) Public Permissioned Data hash IoT data (IPFS) Integrity
44 Lamtzidis et al. [96]IOTA Public Permissionless IoT data IoT data (MongoDB) Integrity, Logging
45 Leme et al. [134] Private Permissioned Data hash RFID data Integrity
46 Leng et al. [110] Custom (2 chains) Public Permissionless Transaction information,
Product information, Personal
data hash
Integrity, Transparency, Logging,
Traceability
47 Liao and Xu [135] Ethereum Public Permissionless Data hash Product information (MySQL) Integrity
48 Lin et al. [136] IoT data, ERP data Transparency, Logging, Traceability
49 Lin et al. [137] IoT data IoT data Integrity, Logging
50 Liu et al. [71] Ethereum Public Permissionless IoT data (anomalies) IoT data (IPFS), Hash Integrity, Logging
51 Lu et al. [106] IoT data , IoT public keys Logging, Access control
52 Madhu et al. [138] IoT data Integrity, Transparency, Logging
53 Mao et al. [89] Ethereum (custom FTSCON) Private Permissioned Product information Integrity, Logging
54 Marinello et al. [139] Animal information Integrity, Logging, Traceability
55 Meidayanti et al. [140] Public Permissionless Animal information Integrity, Logging, Traceability
56 Miloudi et al. [69] Ethereum Public Permissionless IoT data, GIS data (anomalies) IoT data, GIS data Transparency, Logging
57 Murali and Chatrapathy [114] Product ratings Integrity
58 Nadeem Akram et al. [141] Private Permissioned Product information Transparency, Logging
59 Nguyen et al. [142] Ethereum or Private Network Public Permissionless or
Private Permissioned Product information, Pre-orders Manufacturers private data Integrity, Transparency, Logging
60 Nguyen et al. [100]NEO Public Permissioned IoT data, Insurance information Farmers profile Transparency, Logging
61 Orjuela et al. [90] BigchainDB Private Permissioned Product information Logging
62 Osmanoglu et al. [115] Public Permissioned Farmer yield commitment,
Reputation score Integrity
63 Öztürk et al. [143]IoT data Integrity, Logging
64 Paul et al. [144] Ethereum Public Permissionless Farmer information, Product
information, Seed information Transparency, Logging
65 Pincheira et al. [56] Ethereum, Hyperledger Fabric Public Permissioned, Private
Permissioned Product information metadata Integrity, Logging, Incentive
66 Pincheira et al. [86] Ethereum Public Permissionless IoT data Integrity, Transparency, Logging
67 Pincheira et al. [85] Ethereum Public Permissionless IoT data, Tokens (ERC20) Integrity, Transparency, Logging, Assets
68 Pinna and Ibba [104]Job description, Contract,
Wages Integrity, Transparency, Scheduling

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Table 2. Cont.
#Author Blockchain Technology Blockchain Type Data on Blockchain Off-Chain Data Reason for Using Blockchain
69 Pooja et al. [112] Ethereum Public Permissionless Product information, Seed
information Transparency, Logging, Traceability
70 Pranto et al. [65] Ethereum Public Permissionless IoT data (anomalies
periodically), Product
information IoT data (NoSQL) Integrity, Transparency, Logging
71 Prashar et al. [75] Ethereum Private Permissioned Product basic information, IPFS
hash, Hash of previous product Product information, Media files
(IPFS) Integrity, Transparency
72 Raboaca et al. [83] Ethereum Public Permissionless Tokens (ERC20) Sources information, Personal
information (QLDB) Transparency, Assets
73 Rambim and Awuor [145]Farmer information, Product
information Transparency, Logging
74 Ren et al. [58]Ethereum, Custom (ASDS
ethereum-based), Polkadot Public Permissionless,
Private Permissioned Data hash (Custom), Block hash
(Ethereum) IoT data (IPFS) Integrity
75 Revathy and Sathya Priya [146] Ethereum Public Permissionless Transactions information Transparency, Logging
76 Saji et al. [147] Hyperledger Fabric Private Permissioned Product information Integrity, Logging
77 Salah et al. [76] Ethereum Public Permissionless IPFS hash, Seed information,
Product information, Parties
information Media files (IPFS) Integrity, Logging, Traceability
78 Saurabh and Dey [148] IoT data Integrity, Logging, Traceability
79 Shahid et al. [77] Ethereum Public Permissionless IPFS hash Reputation score, Product
information (IPFS) Integrity, Transparency
80 Shahid et al. [78] Ethereum Public Permissionless IPFS hash Reputation score, Product
information (IPFS) Integrity, Transparency
81 Shih et al. [111] Ethereum Public Permissionless Product information, Organic
food inspection agency results Integrity, Transparency, Logging
82 Shyamala Devi et al. [149] Ethereum Private Permissioned IoT data Integrity, Transparency, Logging
83 Smirnov et al. [99] Hyperledger Fabric Private Permissioned Resources, Tasks, IoT data Availability, Scheduling
84 Son et al. [68] Ethereum Public Permissionless IoT data (periodically) IoT data (MongoDB) Integrity, Transparency, Logging
85 Surasak et al. [150] IoT data (OurSQL) Integrity, Transparency, Logging
86 Tan and Zhang [151]Transparency
87 Umamaheswari et al. [152] Ethereum Public Permissionless IoT data Transparency, Logging
88 Vangala et al. [63] Hyperledger Sawtooth Private Permissioned IoT data Credentials (Cloud) Integrity, Logging
89 Wang et al. [153] Custom (JD) Product information Integrity, Logging, Traceability

Appl. Sci. 2022,12, 8061 27 of 37
Table 2. Cont.
#Author Blockchain Technology Blockchain Type Data on Blockchain Off-Chain Data Reason for Using Blockchain
90 Wang et al. [79] Hyperledger Fabric Private Permissioned IPFS hash Product information, Media files
(IPFS) Integrity
91 Wang and Liu [154] Hyperledger Fabric Private Permissioned Product basic information Information, Media files Transparency
92 Wu and Tsai [62] Private Permissioned IoT data Integrity, Logging
93 Xie et al. [66] Ethereum Public Permissionless IoT data (anomalies
periodically), Parent transaction
hash IoT data Integrity, Logging
94 Xie and Xiao [155] Private Permissioned Product information Integrity, Logging, Traceability
95 Xie et al. [156] Hyperledger Fabric Private Permissioned IoT data Integrity, Logging, Traceability
96 Yang and Sun [157] BigchainDB Transaction logs (IPFS), Farmer,
Consumer, Transaction information
(MySQL) Transparency
97 Yang et al. [103]Custom Private Permissioned Machinery information,
Farmland records, Scheduling
data Scheduling, Transparency
98 Yang et al. [107] Public Permissioned RFID Access control Product information, RFID data Access control, Transparency
99 Yang et al. [95] Hyperledger Fabric Private Permissioned Encrypted product private data,
Hash of public data Product public information
(MySQL) Transparency, Logging
100 Yi et al. [158] Ethereum Public Permissionless IoT data Transparency
101 Yu et al. [159] Hyperledger Fabric Private Permissioned Product information, IoT data,
ERP data Transparency, Logging
102 Zhang [109] IoT data, Farmer information Transparency, Incentive
103 Zhang et al. [93] Hyperledger Fabric Private Permissioned Data hash Product information, Product
information encoded Integrity, Logging
104 Zhaoliang et al. [108]Hash of user data,
Authentication information,
Encrypted product information Product information (Cloud) Integrity, Access control, Logging

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4.3. Overall Findings in Brief
In this section, we summarize the main findings of our research, giving detailed
answers to each research question based on the observations made in the included papers
and also providing the limitations. The overall results of our analysis are presented below:
RQ1—Service area
Summary
The most common use of blockchain is for monitoring, product man-
agement, or their combination. In recent years, its use has been proposed for the
certification of various stages of production or processes. In the agricultural sector, it
has also been proposed for the auctioning or trading of products. Finally, there are
proposals for its uses in reputation and reward systems.
Limitations
It is not always obvious for which service area the application is de-
signed. The terminology used is sometimes confusing.
RQ2—Maturity level
Summary
Although the majority of solutions are in the early stages of maturity,
a large percentage of solutions are at a developed level. Unfortunately, no application
on Ethereum has reached the highest level of maturity.
Limitations
A key difficulty was determining the right level of maturity in the
identified solutions. It was often unclear at what stage the provided solutions were,
while in other works, the terminology used for the maturity level has different
meanings to the authors of the papers. This was primarily solved by precisely
defining each maturity level as described in the methodology.
RQ3—Agriculture product
Summary
Most works refer generally to an agricultural sector rather than a specific
product. Moreover, there are more implementations in agricultural products than in
livestock. In addition, a small percentage of papers focus on the goods needed in the
agricultural sector.
Limitations
It could be considered which products can be helped by the use of
blockchain. Moreover, the use of blockchain for utility products (e.g., water) is
limited, but it can help automate processes and precision agriculture.
RQ4—Country
Summary
Most implementations concern Asian countries and, more specifically,
China.
Limitations
There are not enough solutions that focus on the particularities of each
country and its most important products.
RQ5—Blockchain technology
Summary
Most of the solutions are developed on Ethereum and Hyperledger Fabric.
Fewer of the other blockchain technologies have been used.
Limitations
Many conceptual solutions do not mention technology. No solutions have
been presented that propose a cross-chain blockchain among agricultural entities.
RQ6—Blockchain type
Summary
The majority of solutions use a private permissioned blockchain type,
while a large percentage also use public permissionless ones. Finally, in some cases,
both blockchain types are combined.
Limitations
Different agricultural operators have different needs; this is not consid-
ered when choosing the type of blockchain to be used. Moreover, the benefits of each
type are not analyzed on a case-by-case basis.

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RQ7—Data on blockchain
Summary
The most common data stored on blockchain are data from IoT devices.
Due to the cost of data storage, it is often stored periodically (in public blockchains)
or off-chain; only the hash is stored on the blockchain. Finally, due to the trend of
tokens, they are also stored on-chain.
Limitations
In many cases, the cost of data storage is not a consideration, especially
in a public permissionless blockchain. Moreover, many papers have not yet proposed
tokens for the agricultural production line.
RQ8—Off-chain data
Summary
The most common data stored off-chain are again IoT data. The most
common technologies that have been used are mainly IPFS and, to a lesser extent,
BigchainDB and QLDB. Of course, a conventional database can also be used.
Limitations
There are no investigations into how data are secured in case of deletion
from off-chain storage. Moreover, known solutions such as Swarm have also not
been tested.
RQ9—Reason for using blockchain
Summary
Blockchain is mainly used for its inherent features, such as transparency
and integrity, that help solve various security issues. The most common reason is
immutable logging, although this can be costly. Finally, use for scheduling and access
control has been suggested.
Limitations
It is not always clear why blockchain is used and the security issues
it solves.
5. Conclusions
In this article, our goal was to conduct a scoping review with applications of blockchain
technology in the agricultural sector and to identify its advantages. For this purpose, we
used the PRISMA-ScR methodology. With the help of scientific bibliographic databases, we
found the corresponding sources. Systematically, we analyzed 104 research publications,
the largest number of papers in such a study. The research activity in the field started
only in 2017 and is constantly increasing, as shown by the demographic data presented in
the research.
Although the field is still in its infancy and most of the solutions are conceptual,
the research maturity of the papers has shown a development that can be seen in the studies
that have started to be applied to the daily life of agricultural activities. Nevertheless,
blockchain applications in the agricultural sector are still an emerging field with promising
ideas, which is supported by the growing annual distribution of relevant publications.
As the technology matures, other diverse and exciting applications emerge, including
emerging technologies, such as IoT devices, robotics, drones, and many others. Therefore,
researchers are trying to find exemplary blockchain applications in the agricultural sector.
Through this, many exciting solutions have emerged related to traceability, circular econ-
omy, incentive systems, etc. However, given the above, although there are ideas, it is clear
that integrating new technologies in the traditional agricultural sector is a considerable
challenge that should be performed step by step and only with the effective involvement of
directly affected stakeholders throughout the supply chain.
As we have found in our scoping review, blockchain technology shows that it is very
promising in agricultural products; however, some challenges and obstacles need to be
addressed. These are related to the same scalability of blockchain and data storage. Many
of the works we have seen do not consider and use blockchain as storage. However, some
solutions have addressed this issue and suggest using external databases for data storage
and blockchain for data integrity or different purposes. We also believe that the digital
representation of the product is an essential step, something for which the research is still

Appl. Sci. 2022,12, 8061 30 of 37
in its infancy. Finally, an important issue is the privacy (or confidentiality) of farmers’
data and products in the blockchain, which can be studied in future research. Of course,
for the right architecture of all the above and the proper development of decentralized
applications, developers need to better understand the blockchain and the requirements
set by those directly involved.
In the context of our review, we concluded that due to the growing trend of research in
this area, such an extensive search in the future would not be possible. However, in future
iterations of a similar field overview, search queries should be more specific and focus
on specific areas or technological issues, as the number of related studies would have
exponentially increased. Furthermore, our search returned heterogeneous data that were
not easy to classify for conducting the research, even in our case.
In summary, this study can be a starting point for future research into more specific
aspects of blockchain applications in the agricultural sector and serve as a reference and
guide for similar studies in the future. The blockchain is an up-and-coming technology in
various sectors, such as the digitization of the food supply chain, the creation of “smart”
farms, and the certification of products, aiming at consumer confidence. However, obstacles
and challenges still need to be addressed in order for these applications to propagate in
everyday life.
Author Contributions:
Conceptualization, A.S., G.D. and P.S.E.; methodology, A.S., G.D. and P.S.E.;
validation, A.S., G.D., P.S.E. and N.C.T.; formal analysis, A.S., G.D., P.S.E. and N.C.T.; investigation,
A.S.; data curation, A.S.; writing—original draft preparation, A.S.; writing—review and editing,
G.D., P.S.E. and N.C.T.; visualization, A.S.; supervision, G.D. and P.S.E.; project administration,
N.C.T.; funding acquisition, N.C.T. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by the project “Agro4+ Holistic approach to Agriculture 4.0
for new farmers” (MIS 5046239) which is implemented under the Action “Reinforcement of the
Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness,
Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European
Union (European Regional Development Fund).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors would also like to thank the project “Agro4+ Holistic approach to
Agriculture 4.0 for new farmers” for its support.
Conflicts of Interest: The authors declare no conflict of interest.
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