Information asymmetry afects the actors of all the segments of the agri-food supply chain and can arise many problems in the market along the production chain. Transactions of agri-food products are asymmetric because suppliers and buyers have diferent levels of knowledge on the provenance, value, quality, and freshness of food. Collusive relations among the agri-food chain actors, especially between controllers companies and controlled ones, can cause market failures as they influence customers' purchase decisions and severe health accidents when food safety is compromised. This paper proposes using blockchain technology to combat information asymmetry and collusive relations. In addition to transparency, cryptography and trusts, which are natively provided by the blockchain, our approach provides a twofold mechanism for validating crowd sensed data: first, a lightweight syntax validation is run before writing data in the blockchain (providing accountability also thanks to immutability); then, a dedicated smart contract runs semantic validation in scenarios with multiple data sources. This semantic validation may reveal collusive behaviours, downgrade colluding nodes and exclude or down-weight their data in future validations. The smart contract seals data that pass both validations adding metadata on data quality. Results prove the feasibility of our solution on Hyperledger Fabric under the assumption that the majority of nodes are honest. Experimental results demonstrate that our implementation of the twofold validation using smart contracts scales well with the dimension of the blockchain state. Our mechanism may greatly impact Product Certification and Designation of Origin as it may be applied to check specific requirements for raw materials, products, and production processes and protect from the collusion of controlling consortia and certification bodies.
In the globalised society, and above all in the developed economies, the quality and safety of agri-food productions have received increasing attention from the consumer as a result of the evolution intervened in recent years, in terms of production and marketing of products of CEUR vegetable and animal origin in a fresh and transformed state. This structural and functional evolution of the sector is mainly due to some aspects. In fact, in the agri-food sector, there are new products with diferentiated and diferentiable characteristics, including agricultural commodities, vegetable and animal productions, highly-processed and high-service products.
Moreover, in the agri-food system, there is a strong integration of the productive sector with
the final consumer market in terms of information flows, knowledge of markets, and consumer
needs and expectations. The economic and technical literature reports the growing importance
of food quality and safety. These concepts are related to brands, information transparency,
traceability of production and commercial chains, the fight against counterfeiting, and food
fraud [
The contribution of this paper joins together economy and computer science; first, we explain the economic and technical implications of the information asymmetry in the agri-food market, then explore a possible solution to reduce such an asymmetry using blockchain technology. Blockchain has intrinsic traits such as transparency, trust, and traceability; these features help to solve the information asymmetry but, alone, they are not enough to guarantee the data accuracy and validity. Blockchain technology provides data immutability, accountability, and traceability, but it does not guarantee the data quality. In the agri-food sector, data quality is the cornerstone; therefore, a blockchain-based AgriChain platform for data quality is necessary. Using blockchain and smart contracts and applying a novel data validation methodology, we combat information asymmetry and its negative influence on the net value of investments, the ranking of agri-food companies and their capability to access credit for financing their activities. AgriChain uses multiple data sources, in which data are analysed by a set of smart contracts implementing a two-step validation logic (syntactic and semantiscy)n.tTahxevalidation works before data are written on the blockchain; it checks both the data and the user’s identity and guarantees the accountability of the written information. Then, AgriChain smart contract appliesseamantic validation that works after data are written on the blockchain and ‘seals’ them. This validation smart contract fights information asymmetry, providing transparency and data accuracy.
The distortions of information asymmetry in the food market are described in3S. eTchteion
actors and the roles in the agri-food supply chain are discussed in4S.eScetciotinon6 describes the AgriChain methodology for validating and assessing data quality. Experimental setup and results are presented in Sect7i,otnhen related works and conclusions are drawn, respectively in Sections8 and9.
This section briefly introduces the key elements of the proposed architecture, namely ontology and blockchain.
In computer science, ontology is a way to represent semantics (the meaning) through the
definition of categories, properties and relationships expressed through descriptio1n2]l.ogic [
An ontological approach enables or simplifies deductive reasoning, classification,
problemsolving, and the simplification of information exchange among systems. Deductive reasoning is
entrusted to the semantic reasoner, software capable of carrying out reasoning on formalised
knowledge bases. It is capable of elaborating the knowledge base according to some rules to
validate and analyse the knowledge base itself and, therefore, infer logical consequences. In 1999,
the W3C adopted the Resource Description Framework (RDF), which became standard in 2004.
RDF is a data model used to represent ontologies; the atomic data entity is the semantic triple, a
set of three entities: subject-predicate-object. Triples represent a statement on semantic data
(e.g., “Alice is 30”, “Alice knows Bob”). SPARQL Protocol and RDF Query Language (SPARQL) is
a SQL-like query language for receiving and manipulating RDF data. An implementation of
SPARQL is included in Apache Jena, a Java framework for developing semantic web-oriented
applications that include a SPARQL endpoint and supports a specific serialisation format named
Turtle (Terse RDF Triple Language). RDF data validation is entrusted to Shapes Constraint
Language (SHACL), which includes a list of constraints such as cardinality, range of values, etc.
[
Blockchain is a distributed technology that allows for addition-only data storage. Each member of the distributed network (node) has its data replica on which it tracks every resource exchange (transaction) between participants. The transactions are grouped into blocks, linked together through a content hash, to form a chain. Members participate in the validation of transactions in order to add them to the blocks through a distributed consensus algorithm. There are several types of protocols, the most famous being Proof of Work (PoW), Proof of Stake (PoS), and Byzantine fault tolerance (BFT). Ethereum was the first blockchain platform that introduced smart contracts, small programs for validating transactions and performing the computation in a distributed way. Ethereum is a permissionless blockchain where anyone can participate in the network and participate in the consensus protocol.
Conversely, there are permissioned blockchains, such as Hyperledger Fabric (HLF), where participants need special permissions to be part of it. HLF is part of the broader Hyperledger framework, which includes other distributed ledgers, libraries and tools, and the Linux Foundation supports it. Here the smart contracts are called chaincodes and enabqlueertyo read ( operation) and writein(voke operation) the ledger. The ledger is included in a channel; nodes that participate in this channel can read, write and invoke smart contracts. An HLF instance can manage multiple channels and, therefore, multiple ledgers, defining diferent levels of scope for each node.
Since version 2.0, HLF supports chaincodes as an external service. In this case, the chain code management is independent of the node and allows us to define an endpoint where it is executed1. In this endpoint, we can also run more complex services, which the chaincode is capable of invoking, such as in18[] where external chaincodes are used to query external data sources. The call can be made in the single execution of the chaincode, or in case of longer processing times, the chaincode can exploit the oracle para5d].i gInmt[his case, the chaincode emits an event that the service intercepts to start the computation of the request. When the service has finished the processing, it returns the output to the chaincode.
From an economic point of view, it is well known the possibility to score the perceived quality of food products using a scale that spans from optimal to poor without interfering with its potential edibility. However, the hygienic and sanitary safety of the products to the final consumer markets is challenging to evaluate. Consumers have shown great interest in features defining food quality, thanks to an excellent spending capability and a more sensitive contest than in the past. Food quality is a multidimensional and dynamic co1n4]c.eQptua[lity is “a complex value whose definition involves objective and subjective components. For this reason, quality is not a characteristic that can be immediately described or identified. However, it is an idea that each of us has concerning what we need to satisfy a specific need. The more the characteristics of a product correspond to the complex expectations we have concerning it, the more we will be inclined to consider its qua2l5it].yI”t[becomes essential to deepen the analysis on the perception of qualitative aspects, combining technical quality indicators with measures and models of customer satisfaction interpretation in the information economy’s theoretical context. Indeed, placing on the market certified quality products is reflected in an increase in production costs and therefore in prices. Certification requires an estimation of the economic value attributed to the quality perceived by the customers and the evaluation of the premium price concerning the diferent and greater willingness to pay.
Information is an element that afects the functioning mechanisms of the markets, providing a twofold perspective. On the one hand, the “control” and the “management” of the information asymmetry between supply and demand, through the policy of trademarks, certifications, and labelling of agri-food productions. On the other hand, national and international public and private organisations and institutions preside over voluntary standardisation and establish rules and procedures for controlling market transaction costs. They check company and collective brands as precise quality signals, signals of value and contribute to strengthening 1Available ahtttps://hyperledger-fabric.readthedocs.io/en/release-2.4/cc_service.html the necessary operating conditions for the exchange, contributing to the reduction of the information asymmetry typical of imperfect mark1e,t2s0[].
The quality of food production and the economic eficiency of the markets are closely connected and correlated to the growing role of information. This type of situation does not always safeguard the security and correctness of the information and the ability to choose given to informed consumers. From the point of view of the economic production eficiency of the markets, these elements contribute to creating a sort of functional distortions of the agri-food markets that can prevent their correct functioning under the profile of economic theory. These specific conditions seem to simultaneously produce disadvantages for producers and consumers in terms of the natural relationship between supply and demand, oriented to the balance of short and long term markets.
The agri-food supply chain is composed of segments that cooperate to evolve the production process from field to fork. Information asymmetry typically manifests in the last segment of the supply chain afecting final customers but, in many cases, also influences other actors. The various segments concur to a holistic view of the good, including production and transformation processes. In case of partial or inaccurate information, two consecutive parts of the supply chain (e.g., production, transportation, transformation, stock) may experience information asymmetry too. For example, farmers know the history of the grain they grow - origin, timing, and treatments. This information may be hidden to the miller, whose knowledge is limited to storage in silos and the milling process. The same issue related to lack of knowledge occurs between miller and distributors and, more in general, in all the steps between diferent actors. The chain of value and responsibility that links those actors from farm to fork is afected by information asymmetry in all its links.
Farmers and industries need prompt and trusted information to make better decisions for growing or transforming agri-food products. The introduction of blockchain in the agri-food sector has represented a digital innovation aimed at increasing business income by reducing production inputs (and therefore of costs expressed at constant prices) and increasing the outputs (the quantity produced and therefore of revenues expressed at constant prices). Digital innovation is always aimed at increasing the company’s competitiveness and technical and economic eficiency by optimising production factors and reducing variable costs. For example, accurate information on the state of plants brings to savings of water for irrigation, avoiding unnecessary wastes. The same happens for fertilisers and pesticides with knowledge on seasonal trends and infections. These decisions change the structure of production costs and positively afect the entrepreneur’s net income.
The information asymmetry negatively influences production and marketing choices, and the potential problems along the supply chain may lead to market failure. An important issue is related to product certification about the designation of origin. Such certifications are characterised by strict requirements and are guaranteed by consortia and certification bodies. However, between the controlling and controlled entities may arise collusive relations, which are then dificult to discover and strongly afect the market. A recent example is given
syntactic validator semantic validator SPARQL endpoint input data 1 2 3 5
4 semanticSC syntacticSC blockchain peer
the production of ham under the Protected Designations of Origin (PDOs) “San Daniele” and “Parma”, which require the use of a specific breed of pigs. However, a collusive system within the protection consortium eluded controls on the seed of the pigs and, in contrast with the production disciplinary, put on the shelves products whose PDO was not valid. The efects of information asymmetry apply both to product quality and health, as in the cases such as pistachio, whose origin has implications in terms of aflatoxin and ochratoxin and may cause risks for consumers’ healt2h1[]. This example shows that information asymmetry may have diferent facets. The consumer needs to know a product’s provenance, but this information is not suficient if it is not linked to the risks of products from a specific area.
To combat the information asymmetry, we provide AgriChain, a blockchain-based platform for semantic and syntactic validation that executes external smart contracts on HLF (see, Section 2.2). In this way, within a blockchain node, we can run complex services suAcphaachse Jena, a free and open-source Java framework for building semantic applica3t]i,oontshe[rwise impossible to be implemented as legacy smart contracts. It inclSuPdAeRsQaL endpoint, i.e. Fuseki, and asyntactic and semantic validator, i.e. SHACL. As shown in Figure1, each node runs the two smart contracts in yellow that interface with the services mentioned above. We use HLF channels to separate the essential information from metadata and facilitate their operations. Semantic validation works on datasets rather than on a single transaction. The traceability information is stored in tdhaeta channel, while themetadata channel is used to store useful elements for the validation operations.
The syntactic validation takes place before storing the information on the blockchain, implementing filtering on the single input data. The smart consytnrtaacctticSC (see, 1 in Figure1) receives as input the data and performs a signature validation. This step is shown in Algorithm1, where takes as input parameter and passes i t t(o) function (see, Lin2e). If that check is successful, we continue with syntactic validation, calling () function (see, Line3), as described in Sectio6n, to invoke thesyntactic validator service (see,2 in Figure1). This validation has to be customised as needed and depends on the context of the application, for example, to verify tℎh at a” “field has a numeric value expressed i n . When the validation is successful, we m ap into a format (see, Line4) valid to be loaded oSnPARQL endpoint (see, 3 in Figure1). We assume that the reference ontology is preliminary written on the blockchain and importedSiPnAtRoQtLhe endpoint before starting the data collection process. Writing the ontology on the blockchain guarantees interoperability and transparency in the definitions of products and links between them. Finally, we also store on data channel (see, Lin5esand6).
Algorithm 1 syntacticSC Require: as input data 1: procedure syntacticSC( ) 2: if signatureValidation( 3: if syntacticValidation( 4: ← mapping( 5: putSPARQL( ) 6: putBC( ) 7: end if 8: end if 9: end procedure 5.1.1. Semantic validation ) then
) then ) The semantic data validation process uses SHACL shapes, deriving from the on2tWoleogy. assume that they are already present on the blockchain and used by the smartscemonatnr-act ticSC. The semanticSC, as shown in Algorithm2, receives as input the paramete rs , that is, the SPARQL query which determines the subject of the validationℎ, and, the identifier of a shape stored on the blockchain used in the validation. When this smart contract is invoked, it retrieves the from theSPARQL endpoint (see, 4 in Figure1), using() function with parameter (see, Lin2e). Similarly, we retrieves tℎhe shape from blockchain with () function (see, Lin3e). Then we forwar d andℎ shape toSHACL validator (see, 5 in Figure1). Here, the () function calls tsheemantic validator service (see, Line 4) which performs a semantic validation and gives bac k the. Now, , along with 2We generated the SHACL shapes using the Astrea thottopls,://astrea.linkeddata..Tesh/e shapes have been tuned, and we added missing validation elements from the ontology, such as the cardinality range. , are examined by acalculateScore() function (see, Lin5e) which scores the validation performed. The single application defines the calculation of the score and its metric; for example, the closer the harvesting coordinates of diferent olives are, the more accurate the result that the crop belongs to an exact agricultural land. At the e,nℎd , , , and are written on metadata channel as proo(f)via function (see, Lin6e).
Algorithm 2 semanticSC Require: as input query for SHACL validation Require: ℎ as input id of SHACL shape 1: procedure semanticSC( , ℎ ) 2: ← getSPARQL( ) 3: ℎ ← getBC(ℎ ) 4: ← semanticValidator( ,ℎ ) 5: ← calculateScore( , ) 6: putBC( , ℎ , , ) 7: end procedure
The reasonerSC interfaces the blockchain with the reasoning service. When this smart contract is invoked, it queriesStPhAeRQL endpoint (see 3 in Figure1) to obtain the dataset to forward to the reasoner, indicated 4w).itWhhen the reasoner finishes its processing (s5e)e, the smart contract stores the result on the blockchain. If the result leads to new inferred triples from the initial dataset, the new data is updatedSiPnARthQeL endpoint invokingsyntacticSC. In such a case, the initial data are stored on the data channel, and the inferred information goes on the metadata channel.
The agri-food sector includes multiple supply chains for the diferent agricultural products: tomatoes, wine, dairy, olive oil, etc. These supply chains involve many actors with diferent roles, and in most cases, they hold contrasting interests. Agricultural entrepreneurs, transformation industries, transport, logistics, and great and small distribution are exemplary actors that appear in many agri-food supply chains. However, any chain has its peculiar actors with specific needs and roles. For example, in the simplified model of the olive oil supply chain shown in F2i,gure there are farmers, olive growers’ cooperatives, warehouses, shops, and customers as the main actors. These actors typically provide data through human operators, which are not trusted by default. To solve the problem of mistrusted operators, the authors propose to use IoT devices. However, this strategy shifts the point of trust from humans to IoT devices. IoT sensors are owned and maintained by those actors indicated above and can be maliciously manipulated according to their specific interests. To guarantee data quality, AgriChain leverages the double validation indicated above, invoking dedicated smart contracts.
The input syntactic validation, performed by smart consytnrtactticSC (see, Pseudocode1), checking that the transaction contains specific fields, as exemplary shown in L1i,stininclguding the actor’s signature. The smart contract checks multiple signatures if multiple actors are olive growers' cooperative untrusted data olive milling oil selling bottle farmers warehouse shops customers product and processes
actors IoT devices and operators (data
sources) smart contract validation logic blockchain
trust transparency traceability involved in the transaction. This validation is performed on a trabnesfaocretiboening written on the blockchain. This preliminary validation guarantees accountability because each piece of data is linked to an accountable entity, but still, it does not protect from the ‘garbage in, garbage out’ problem. In other words, this lightweight syntax validation checks the identity of the data provider, the timestamp, and other metadata without guaranteeing ‘semantic’ validity. { } ” a c t o r ” : {
” s i g n a t u r e ” : ” e b f 3 d 6 a 0 e 5 4 d 2 4 9 f f . . . ” } , ” r e s _ d e t a i l s : ” : { ” res_name ” : ” o l i v e s 0 1 @ f i e l d 0 1 ” , ” hasGeoTag ” : true , ” hasWeight ” : t r u e } , ” d a t a ” : { ” l a t ” : 3 8 . 1 2 0 2 4 0 , ” lon ” : 1 3 . 3 5 7 3 8 8 , ” kg ” : 10 } , ” t s ” : ”2020 −05 −30 T16 : 0 6 : 4 4 + 0 1 : 0 0 ”
Listing 1:Syntactic validation - Fields extracted from the transaction.
The second check involves both syntactic and semantic validation; in what follows, we stress the semantics aspects. Here, the smart contsermacatnticSC (see, Pseudocode2) takes care of the validation on a more extensive set of data that, grouped, have a special meaning; the validation logic depends on the specific supply chain and the meaning of data, in our experiments we focused on the geographical origin of the olive oil product. Unlike the typical blockchain validation, our semantic validation is perfoarftemr edthe data is written on the blockchain, it is triggered by new data arrivals that are semantically linked to the previous ones. For example, the geographic coordinates provided by several harvesting operators through their smartphones and IoT devices with GPS receivers are in List1i,npgroviding the location of the product and farm field01 . As shown in Figure3, the syntax validation smart contract uses clustering to estimate the position (the mean of the majority cluster) from malicious and colluding nodes (in red).
When an agri-food related business choices to use blockchain technology to implement its food supply chain in some or all aspects, it is choosing to undergo some change. Change is not always good for business, so why should a business decide to switch to a blockchain-based solution? Because using blockchain expresses the company care about transparency, thus inspiring old customers to possibly buy more products and/or new ones to switch from another brand to this one. Of course, every kind of IT infrastructure comes with costs of installation and maintainability. We propose those costs to be proportionally assigne dintovotlhveed actors. This solution could be thought of as a blockchain-based pay-per-use like a subscription system.
Part of the platform presented in this paper was proposed withDinEMtEhTeER3 project, which
leads the digital transformation of the European agri-food sector through the rapid adoption of
advanced IoT technologies, data science and smart farming, ensuring its long-term viability and
sustainability. Our blockchain currently runs withDiEnMtEhTeER ecosystem, and the project
partners can invoke its services. A fundamental part is the semantic model, used as a common
language between diferent project entities. It is based on the GS1 vocabulary, extended, revised
and refined to be able to describe an entire supply chain. We exemplary show the olive oil
supply chain (see, Figure4), where we have extended thges1:FoodBeverageTobaccoProduct4
class withsb:OliveOil5 to be able to map the entire process. In addition to the interoperability
ofered by the semantic model and its mappings with other ontologies, the platform ofers APIs
compliant with the OpenAPI standard. Seeing the generality of the platform, we, as a case
study, have implemented the validation of olive harvesting in the olive oil supply chain. Within
theSHACL validator, we have added a clustering algorithm,DtBhSeCAN [
As introduced in Sectio6,nwe used Hyperledger Fabric to implement our model partly and to give some experimental results in terms of performances. Our test network was composed of two Fabric organisations, having two peers each. Moreover, we used the RAFT alg1o7r],ithm [ which is the default consensus protocol for Hyperledger Fabric. RAFT is a CFT (Crash-Fault Tolerant), but it can be easily substituted with a BFT (Byzantine Fault Tolerant) as Fabric has a modular approach to the consensus proto4c]o.l [
Thus we had five nodes running for consensus purposes. The blockchain was deployed on a single host configuration on a machine with the following specs©:IXnetoenl© CPU E5-1660 v3 @ 3.00GHz with 32 gigabytes of RAM. Figur5eshows, out of 100 consecutive invocations of the smart contracsetmanticSC, the time spent by thDeBSCAN algorithm for clustering (see, Figure 5a), the number of clusters found (see, Fig5ubr)e, and the number of entries used DbyBSCAN (see, Figure5c). At each invocation, we assume that the number of entries has increased by 1 unit, sosyntacticSC has inserted a new entry into the blockchain. We can see how the analysis of 100 points, the most computationally expensive part, uses about 8 ms, with is compatible with the smart contract execution. The clusterisation of the terrain, with about 75 points, required 6 ms.
The problem of information asymmetry in food traceability has multiple facets that have been traditionally tackled singularly and using old paper documents and product specifications. Our approach towards information asymmetry is to improve transparency under multiple points of view: economy, blockchain technology, data quality.
From an economic point of view, it is well known the possibility to score the perceived
quality of food products using a scale that spans from optimal to poor without interfering with
its potential edibility. However, the hygienic and sanitary safety of the products to the final
consumer markets are challenging to evaluate. Consumers have shown great interest in features
defining food quality, thanks to a greater spending capability and a more sensitive contest
than in the past. Food quality is a multidimensional and dynamic co1n4]c.eQptua[lity is a
complex feature made by objective and subjective components. For this reason, quality cannot
be immediately described or identified, but it is a subjective idea that involves personal needs.
The more the characteristics of a product match our expectations, the more we will be inclined
to consider its qualit2y5][. It becomes important to deepen the analysis on the perception
of qualitative aspects, combining technical quality indicators with measures and models of
customer satisfaction interpretation in the information economy’s theoretical context. Indeed,
placing on the market certified quality products is reflected in an increase in production costs
and therefore in prices. Certification requires an estimation of the economic value attributed
to the quality perceived by the customers. This requires the evaluation of the premium price
concerning the diference and greater willingness to pay. Information is an element that afects
the functioning mechanisms of the markets, providing a twofold perspective. On the one hand,
the “control” and the “management” of the information asymmetry between supply and demand,
through the policy of trademarks, certifications and labelling of agri-food productions. On
the other hand, national and international public and private organisations and institutions
preside over voluntary standardisation and establishing rules and procedures for controlling
market transaction costs. Company brands, collective brands, signals of quality and value work
as media communication and contribute to strengthening the operating conditions necessary
for the realisation of the economic exchange, contributing to the reduction of the information
asymmetry typical of imperfect market1s,)2[
From the point of view of the economic eficiency of the product markets, these elements
contribute to creating a sort of functional distortions of the agri-food markets that can prevent
their correct functioning under the profile of economic theory. These specific conditions
seem to simultaneously produce disadvantages for producers and consumers in terms of the
natural relationship between supply and demand, oriented to the balance of short and long
term markets. In fact, in22[
Quality of food production and the economic eficiency of the markets are closely connected and correlated to the growing role of information. This type of situation does not always safeguard the security and correctness of the information and the ability to choose given to informed consumers. The central role of the agri-food sector requires quality of data because erroneous, malicious, and missing information afect the food supply chain in terms of quality and safety. This paper presented AgriChain as a mechanism for validating data syntactically before being included in the blockchain and semantically before being sealed. These two validations are executed through a distributed logic, implemented with one or more dedicated smart contracts. Typically the blockchain is the preferred technology when seeking trust, transparency and traceability among actors who do not trust each other or have contrasting interests. We demonstrated how AgriChain goes beyond this vision on data management, breaking the simplistic concept that data written on the blockchain are trustful because they have been validated in advance. Indeed, Agrichain performs only a lightweight validation before including the information into a block; this only guarantees accountability and syntax consistency. From the semantic point of view, the second validation guarantees first data cleaning second data quality assessment. AgriChain performdsata cleaning applies clustering algorithms implemented as smart contracts on data collected through crowd-sensing. Standard data cleaning methods aim at detecting and removing repeated entries, detecting outliers, checking data volumes. In general, such methods do not deal with malicious data sources. Then, AgriChain smart contract checks accuracy, timeliness, completeness, uniqueness, and consist6e]nacnyd[ provides KQIs, Key Quality Indicators which are added, as metadata, as a data seal on the blockchain. This paper presented a new methodology for using smart contracts to enforce a twofold validation and guaranteeing the quality of data for food traceability.
The authors would like to thank the SNAPP laboratory of Security, Network Applications and Positioninghttp://www.unipa.it/SNAPPLaba/t the Department of Engineering of the University of Palermo, and SEEDS s.r.l. for experimenting on SeedsBit plathftotrpms://seedsbit.co m./This work has been partially supported by the H2020 EU DEMETER prohjtetcpts://h2020-demeter. eu/.