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 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, for which products, as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR methodology. The purpose of conducting the scoping review is to identify the evidence of 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 studies in the design phase, several experiments have been made, 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, circular economy, data privacy, product certification, and reputation systems.

Full text

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


Citation: Sendros, A.; Drosatos, G.;
Efraimidis, P.S.; Tsirliganis, N.C.
Blockchain Applications in
Agriculture: A Scoping Review.
Preprints 2022,1, 0.
https://doi.org/10.3390/appxxxxx
Received: 20 June 2022
Accepted:
Published:
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affili-
ations.
1Institute for Language and Speech Processing, Athena Research Center, Xanthi 67100, Greece;
asendros@athenarc.gr (A.S.); pefraimi@athenarc.gr (P.E.); tnestor@athenarc.gr (N.T.)
2
Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece;
asendros@ee.duth.gr (A.S.); pefraimi@ee.duth.gr (P.E.)
*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 solu-
tions are now being proposed to address various problems in different domains and 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, for which products,
as well as the level of maturity of the respective approaches. The study follows the PRISMA-ScR
methodology. The purpose of conducting the scoping review is to identify the evidence of 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 studies
in the design phase, several experiments have been made, 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, circular economy, data privacy, product certification, and
reputation systems.
Keywords: Blockchain; Distributed Ledger Technology; Agriculture; Scoping Review; PRISMA-ScR
1. Introduction
At the dawn of 21
st
century, the agricultural industry, which is still rapidly growing,
represents a turnover of 3.5 trillion dollars [
1
], but 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 GATT and WTO [
3
,
4
]. However, there is no standard global agricultural protocol
shared among agriculture participants, only regional regulations, which leads to misunder-
standings 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 breach
or loss of data [
5
,
6
]. Therefore, the trust between them is an essential part of reducing the
risk of the supply chain safety [7].
The importance of all the above becomes more understandable 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 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

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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 outcomes, 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 in 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
]. Especially,
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 architec-
ture 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
]. 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 are Practical
Byzantine Fault-Tolerance (PBFT) and Raft consensus algorithm [
21
]. Finally, the need
for blockchain in particular application domains has led to the recent trend of creating
application-specific consensus algorithms suitable for specific tasks (e.g., IoT, supply chain,
and trading) [20].

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1.2. Blockchain Technology in Agriculture
Following this revolutionary idea, the prospects of blockchain evolved rapidly, with
blockchain being used in areas other than cryptocurrencies, and smart contracts [
22
] playing
a central role in 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 more efficiently. However,
blockchain is an infrastructure that can additionally offer data immutability as well as
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
agriculture 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]. Also, 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 all
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 (ambrosus.io),
and TE-FOOD (te-food.com)): Consumers and regulators can ensure the origin of the
products. Also, 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 (fairchain.org)): Blockchain can be
used to reduce intermediaries and distribute profits transparently to producers.
•
Product insurance and claiming compensation (e.g., Etherisc (etherisc.com)): Smart
contracts can replace insurance documents and schedule insurance activation accord-
ing to IoT sensors. All the transactions are transparent and visible from the 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
], the cost and performance of blockchain data stores [
34
], and privacy is-
sues 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, aspects of blockchain use in the agricul-
tural 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 is 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
in agriculture, and to identify existing knowledge gaps. Furthermore, this work may lead
to more detailed 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]
, which includes 49 papers, while in 2021

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the same main author, Kamilaris et al.
[38]
, published a book chapter which includes this
time 80 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 technology when
conducting their research. This is mainly due to the recent explosion of blockchain use and
the corresponding increase of 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, in relation to previous related work, is novel in several aspects.
First, our research is the most comprehensive literature review that has been done 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. Thirdly, 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. Such were the data 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 data on-chain/off-chain, off-chain technologies used, 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.
2. Methods
2.1. Goal and Research Questions
Our scoping review is conducted to map the research done in this area systematically
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 technol-
ogy 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?

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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. Is 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
interdisciplinary 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 method-
ology [
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.
Also, 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 grey 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
also provides advanced search and is easy to export. The following query was performed
on April 9th, 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 done on April 14th, 2021
and the following queries were used:

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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 our 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 list of 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
variables 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 where 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
papers were classified into the specified categories. Finally, the following additional data
items were exported:
•
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
simulate 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

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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; and (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
specifically, this categorization is as follows (a) Public Permissionless: in this case
both the transaction data and the participation in the consensus algorithm are acces-
sible to all those who participate in the network (such as Ethereum and Bitcoin); (b)
Public Permissioned: unlike public permissionless blockchains, while the transac-
tion data is open to everyone, the transaction validation involves specific users who
have been authorized (such as Ripple and private versions of Ethereum); (c) Private
Permissioned: 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 is 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 publica-
tions.
•
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 logging, integrity, transparency, access control, etc.
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
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 remove 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 conferences proceedings, 48 were not scientific papers,

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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 complete
paper analysis. During the second screening, 22 papers were excluded as 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 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 papers, in our review, have been 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.
Figure 3presents the number of papers per publisher that was finally included in
our scoping review. IEEE holds the highest number of papers, corresponding to the 42%
(44 papers) of all 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.
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 eight
journal 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), 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

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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 scoping
review.
44 (42%)
13 (13%)
13 (13%)
11 (11%)
5 (5%)
4 (4%)
14 (13%)
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.
of Advanced 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).
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.

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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.
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 7depicts the classification of 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
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

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solutions that belong to the 2% and refer to corn, soybean, oil, wine, chickens, and cows.
Other products 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
indirectly, such as water irrigation (6%), photovoltaics (2%), and wastes (1%).
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 referred to more than one solution,
specifically in 2 papers (5%).
Analysis of each source identified more specific attributes about the blockchain tech-
nology framework (if any) used, the blockchain type utilized in the applications, the specific
data stored on-chain and off-chain, and reasons for using blockchain; a summary of data
charted is shown in Table 2.
Figure 9shows the various blockchain technology frameworks (RQ5) that are con-
sidered by the proposed solutions. The most used blockchain technology is 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.
Regarding the types of blockchain (RQ6) utilized in the identified solutions, most
of them (36%) use private permissioned blockchains, as shown in Figure 10. Also, a

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#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.
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.
significant percentage of papers (28%) use public permissionless blockchains, while 5%
of 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.
Analysis of each source identified the data stored in the blockchain in agricultural
applications (RQ7). This data varies depending on the solution proposed by each paper, as
shown in Figure 11. The most common type of stored data, as reported on 43 papers (41%)
(Figure 11a), is 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 6 papers. In contrast, in another 6
solutions, the data is stored in the blockchain periodically during the day. The aggregate
data is 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

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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 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.
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) Popular data types stored in blockchain.
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) A word cloud of the total data stored in blockchain.
Figure 11. Types of data stored in the blockchain in the papers included in our scoping review.
The data stored off-chain (RQ8) in the identified solutions is shown in Figure 12. From
the 104 research solutions proposed, 37 of them (36%) mention at least one external storage.

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Most of the data stored off-chain is 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
log (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%). Also, other distributed storage systems are BigchainDB (2%),
QLDB (1%), OurSQL (1%) and a not specific distributed database (1%). Finally, there are
solutions that use SQL (4%) and NoSQL (3%, including MongoDB) databases, as well as
cloud storage (2%) and BigQuery (1%).
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.
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 logging (72%).
Also, it is commonly used to achieve transparency (61%), integrity (50%), and traceability
(18%). Other uses include ensuring access control to devices or users (6%), scheduling (5%),
storage assets (4%), data availability (3%), and finally for incentives (1%).
An overview of the data charting keywords that were identified by our detailed
analysis according to the research questions (RQ1 –RQ9) explored in this scoping review
is presented as a mind map in Figure 14.

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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.
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.

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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 paragraphs.
One of our primary findings was the variations of 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 Hypeledger 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 33papers ). Apart from the
above in the use of blockchain in the agricultural sector, other technologies have been used
less frequently, such as Hypeledger 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.
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%). This
data is 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 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 cor-
responding category of solutions 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 work [
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 did the 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
physical object or system, usually in multiple stages of its life cycle [
81
]. As Pylianidis et al.
[82]
points 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 research that has been done represents products as tokens (following 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 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 IoT device that

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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 done 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 description and contract to define the
work of some farmers, machine information that can be rented to farmers, RFID data or
GIS sensors, blockchain access rules, drone data, pre-orders that may be available, product
rating 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.
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 done some simulations or have been partially experimental (not
in the blockchain). 18% of papers have done experiments to test 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% is 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
onwards, 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 traceability of products and 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 3 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.
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.
In this scoping review, we also research the reason for using blockchain in the agricul-
ture sector (RQ9). As a result, most solutions use blockchain for its inherent characteristics,
such as data transparency and integrity. This happens at 61% and 50% respectively. Also,
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

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using blockchain, a typical process is storing product information and tracking information.
This 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,
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. Insted, 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 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 robots 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 public keys and access policy. 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].
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
management. This is observed in 55% and 75% of the papers, respectively. A unique feature
is that most applications (61%), that have been created for products 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 is 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 integrity
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

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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. 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 was
not easy to classify for conducting the study, even in our case. Finally, as a limitation, it
should be noted that because 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.
An additional difficulty was to determine the correct level of maturity in the identified
solutions. It was often shady at what stage the provided solutions were, while in other
works the terminology we used for the maturity level has different meanings for the
authors of the papers. This was primarily solved by precisely defining each maturity level
as described in the methodology. Respectively, we had similar limitations in the findings of
other research questions in which in several works they were quite general and did not
give us the specific data.
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 done step by step and only with the effective involvement of
directly affected actors throughout the supply chain.
As we have found in our scoping review, blockchain technology shows that it is very
promising in agricultural products; 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 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

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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 retrieved papers will not be manageable.
Furthermore, our search returned heterogeneous data that was 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 spread in
everyday life.
Author Contributions: Conceptualisation, A.S., G.D. and P.E.; methodology, A.S., G.D. and P.E.;
validation, A.S., G.D., P.E. and N.T.; formal analysis, A.S., G.D., P.E. and N.T.; investigation, A.S.; data
curation, A.S.; writing—original draft preparation, A.S.; writing—review and editing, G.D., P.E. and
N.T.; visualisation, A.S.; supervision, G.D. and P.E.; project administration, N.T.; funding acquisition,
N.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.

21 of 36
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
1 Abraham and
Santosh Kumar [116]
2020 Conference Management Conceptual India
2 Ahmed et al. [117] 2020 Conference Management (fertilize) Conceptual Bangladesh
3 Alonso et al. [101] 2020 Journal Monitoring, Management (IoT
platform)
Partial Experimental (not in the
blockchain)
Milk Spain
4 Arena et al. [102] 2019 Conference Certification (olive) Experimental Extra virgin oil
5 Arshad et al. [60] 2020 Conference Monitoring (with Access control) Partial Experimental (not in the
blockchain)
Pakistan
6 Awan et al. [118] 2020 Journal Monitoring (IoT with energy
efficiency)
Simulation (Matlab)
7 Awan et al. [72] 2020 Conference Monitoring, Management (crop) Simulation (Matlab) Crops, Grains Pakistan
8 Bakare et al. [91] 2021 Conference Management (subsidies) Prototype India
9 Balakrishna 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 manufucture) 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 (concensus
protocol)
27 Enescu et al. [54] 2020 Journal Management, Trading (energy) Proof of Concept Photovoltaic, Water Romania
Continued on next page

22 of 36
Table 1 –continued from previous page
# Author Year
Source Type
Service Area Maturity Level
Agriculture Product
Country
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
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 invesion),
Management (crop)
Partial Experimental (not in the
blockchain)
Crops
36 Iswari et al. [129] 2019 Conference Monitoring, Management Conceptual Coccoa 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)
Continued on next page

23 of 36
Table 1 –continued from previous page
# Author Year
Source Type
Service Area Maturity Level
Agriculture Product
Country
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
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 sytem) 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
Continued on next page

24 of 36
Table 1 –continued from previous page
# Author Year
Source Type
Service Area Maturity Level
Agriculture Product
Country
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)
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
1 Abraham and
Santosh Kumar [116]
Hyperledger Fabric Private Permissioned Farmer information Transparency, Logging
2 Ahmed et al. [117] Hyperledger Fabric Private Permissioned Fertilizer information Integrity, Logging
3 Alonso et al. [101] IoT data hash IoT data (BigQuery) Integrity, Traceability,
Logging
4 Arena et al. [102] Hyperledger Fabric Private Permissioned IoT data Integrity, Logging,
Traceability
5 Arshad et al. [60] Hyperledger Fabric Private Permissioned IoT data, Policy headers,
Access records
Access control, Integrity,
Logging
6 Awan et al. [118] IoT data Integrity, Logging
7 Awan et al. [72] IPFS hash Product growth
information, Media files
(IPFS)
Integrity, Availability
8 Bakare et al. [91] Custom Public Permissioned Farmland records Transparency, Logging
Continued on next page

25 of 36
Table 2 –continued from previous page
# Author
Blockchain Technology
Blockchain Type Data on Blockchain Off-chain Data
Reason for using Blockchain
9 Balakrishna 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
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] Hypeledger Fabric Private permissioned Iot data, Product
information, Access policy
Integrity, Logging, Access
control
Continued on next page

26 of 36
Table 2 –continued from previous page
# Author
Blockchain Technology
Blockchain Type Data on Blockchain Off-chain Data
Reason for using Blockchain
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 invesion),
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
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
Continued on next page

27 of 36
Table 2 –continued from previous page
# Author
Blockchain Technology
Blockchain Type Data on Blockchain Off-chain Data
Reason for using Blockchain
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
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
Continued on next page

28 of 36
Table 2 –continued from previous page
# Author
Blockchain Technology
Blockchain Type Data on Blockchain Off-chain Data
Reason for using Blockchain
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
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
Continued on next page

29 of 36
Table 2 –continued from previous page
# Author
Blockchain Technology
Blockchain Type Data on Blockchain Off-chain Data
Reason for using Blockchain
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

30 of 36
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