=Paper=
{{Paper
|id=Vol-3791/paper15
|storemode=property
|title=Integrating Computer Vision & Blockchain for Enhanced Saffron Evaluation: A Focus on Filament Curvature Assessment
|pdfUrl=https://ceur-ws.org/Vol-3791/paper15.pdf
|volume=Vol-3791
|authors=Mohammad Rowhani Sistani,Pierluigi Gallo,Maria Timoshina
|dblpUrl=https://dblp.org/rec/conf/dlt2/SistaniGT24
}}
==Integrating Computer Vision & Blockchain for Enhanced Saffron Evaluation: A Focus on Filament Curvature Assessment==
https://ceur-ws.org/Vol-3791/paper15.pdf
Integrating Computer Vision & Blockchain for
Enhanced Saffron Evaluation: A Focus on Filament
Curvature Assessment
Mohammad Rowhani Sistani1,2,3 , Pierluigi Gallo1,4 and Maria Timoshina2
1
University of Palermo, Italy
2
SEEDS s.r.l., Italy
3
University of Camerino, Italy
4
CNIT - Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Italy
Abstract
In this paper, we present a study on the curvature analysis of saffron filaments using computer vision.
The goal is to develop a reliable method for detecting altered saffron by analyzing the curvature of saffron
filaments in captured pictures using image processing. Saffron can be altered through processes such as
pressing and ironing, one of the usual frauds on the saffron supply chain. We explore the integration
of blockchain technology in the saffron supply chain to enhance traceability and ensure economic and
societal sustainability.
Keywords
Blockchain, Saffron, Computer Vision, Supply chain, Image processing
1. Introduction
Saffron, the most expensive spice in the world, derived from the Crocus sativus flower, is a highly
sought-after spice known for its distinct flavor, aroma, and vibrant color. The harvesting and
processing of saffron involve intricate methods, and the quality of saffron is often determined
by the characteristics of its filaments [1]. Saffron, often referred to as ”red gold” holds a special
place not only in the culinary world but also in traditional medicine and cultural practices.
Its scarcity, along with the intensive harvesting process, contributes to its high market value.
Undoubtedly, the primary factor driving its value is the extensive requirement for manual labor
at constantly increasing costs, compressed into a limited number of days and just a few hours
each day.
Additionally, post-harvest, a dehydration treatment is essential to transform the stigmas of
Crocus sativus L. into saffron spice. This dehydration process results in the stigmas losing
approximately 80% of their original weight [2].
Another reason for the high price and value of saffron is its low cultivation area which leads to
its low production in the world. Iran, Spain, and the Republic of India are the largest producers
6th Distributed Ledger Technologies Workshop (DLT2024), May, 14-15 2024 – Turin, Italy
Envelope-Open Mohammad.rowhanisistani@unipa.it (M. R. Sistani); pierluigi.gallo@unipa.it (P. Gallo);
maria.timoshina@seedsbit.com (M. Timoshina)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
of saffron in the world, collectively responsible for around 90% of global production, equivalent
to approximately 300 tons annually [3].
The majority of saffron production in India is concentrated in the Jammu and Kashmir
region. Currently, Kashmiri saffron encounters challenges in the selling process, primarily
stemming from issues such as the absence of standardization, certification, and quality assurance.
Adulteration emerges as a significant concern contributing to a decline in saffron demand, it
leads to a deterioration in its overall quality, reduces customer trust, and causes health problems
for consumers.
In our study, we delve into how computer vision can be a game changer for identifying the
characteristics of saffron. The fundamental concept involves endowing machines with the
ability to recognize what is apparent to humans, particularly in inspecting saffron filaments
and distinguishing its color, length, and straightness. The objective entails instructing com-
putational systems to visually perceive and interpret images or videos akin to an experienced
specialist, albeit with the precision and steadfast attention characteristic of algorithms [4]. By
leveraging image processing, a key component of computer vision, we can notably enhance
both the precision and efficiency of the saffron evaluation process. This analysis, a facet of
image processing, enhances the reliability and speed of quality assessment by reducing noise,
improving contrast, and extracting pertinent features from the saffron imagery. Our objective
is to implement a repeatable, automatic, reliable, and foolproof system resilient against errors
and human oversight, capable of promptly assessing the quality of saffron. This effort is aimed
at maintaining standards and providing assurance to consumers regarding the authenticity of
the product [5].
The pervasive challenge of ensuring product authenticity and data integrity in the face of
manipulation calls for robust solutions. In response to this, technology emerges as a valuable
tool. Blockchain offers a transparent, traceable, and decentralized ledger system that is tamper-
resistant by design. This secure framework holds particular promise for enhancing transparency
and ensuring the traceability of high-value goods such as saffron. Products like saffron are
susceptible to counterfeiting, both in terms of physical products and associated data throughout
the saffron supply chain. By integrating blockchain into the saffron supply chain, we can
significantly reduce the risk of fraud and protect the data from being altered illicitly. The
technology not only supports secure transactions but also paves the way for trusted decision-
making among multiple stakeholders in the supply chain, industry, and consumers.
Blockchain technology transforms the concept of a shared registry into a tangible reality
across diverse application domains, spanning from cryptocurrency to potential implementations
in decentralized, resilient, and trustworthy decision-making within multi-stakeholder industrial
systems. Particularly notable is its utilization to enhance sustainability, such as establishing a
high-trust supply chain, which impacts economic viability, environmental preservation, and
social equity, which are the primary focus of exploration in this study.
2. Related works
A diverse range of research and innovation fields offers a wealth of novel perspectives, sophis-
ticated techniques, and cutting-edge technological developments. This section explores the
reduced human
errors,
interferences and
Blockchain frauds Computer
Network Vision
Sustainability
Traceability and
Transparency
collecting features of
information saffron
from farm to strands
fork
Saffron
Products
Figure 1: The integration of three combinations of saffron, computer vision and blockchain
current body of knowledge on saffron analysis in related works, several projects in various
image processing applications, computer vision applications in food quality assessment, and the
revolutionary potential of blockchain technology in supply chain management. These linked
works demonstrate the dynamic nature of research in these fields and provide the framework
for comprehending the complex strategies and solutions used in various domains.
2.1. Saffron Analysis Literature
This paper extends a previous work on computer vision and blockchain for saffron in [6]. The
paper focuses on enhancing the transparency, traceability, and quality control of the saffron
supply chain through the integration of blockchain technology and computer vision, introducing
objective quality assessment focusing on filament color and length moving beyond subjective
human assessment. The system validates data with a blockchain network on Multichain to
ensure a secure and tamper-resistant tracking mechanism across the supply chain. The present
paper extends that work by analysing curvature of filaments and moving forward towards a
methodology for saffron identification.
In [7] the authors propose a saffron quality classification based on its aroma. The paper
addresses a critical issue in the saffron industry, where the high value of saffron makes it suscep-
tible to adulteration. Adulteration methods, such as adding cheaper materials or immersion of
saffron in substances like honey or oils, pose challenges for both spice industries and consumers.
The paper introduces an innovative approach utilizing an electronic nose system with metal
oxide semiconductor sensors for detecting saffron adulteration. The electronic nose captures
aroma fingerprints, and the data are analyzed using principal component analysis (PCA) and
artificial neural networks (ANN).
The work in [8] offers significant insights into theme of saffron adulteration detection high-
lighting such methods as 1H NMR spectroscopy and multivariate data analysis. It underscores
the absence of a robust method to distinguish pure high-grade saffron from adulterated samples,
motivating the study. The paper asserts that the combination of 1H NMR spectroscopy and
multivariate data analysis serves as a potent approach for detecting saffron adulteration. This
method provides a rapid, minimally invasive, and comprehensive detection tool but spectroscopy
is not available at customers’ premise neither in many industrial environments.
2.2. Image Processing Benefits
The research on the relationship between computer vision and food quality assessment is
particularly relevant to our analysis.
In [9] it is listed a range of studies and applications demonstrating computer vision technol-
ogy’s transforming power in the food sector. The volume delves into various approaches and
strategies employed in utilizing computer vision to assess food product quality, from dairy and
meat to fruits and vegetables. The authors highlighted technical solutions and methodologies
including the use of computed tomography and magnetic resonance imaging in food science,
including their ability to provide detailed internal images of food products. This methodology is
useful for studying foods’ internal structure, such as salt distribution in dry-cured ham or fat in
meats. MRI, with its excellent renditions of soft materials, is suitable for visualizing most food
objects, monitoring dynamic changes as foods are processed, and characterizing the physical
state of water in frozen corn, among other applications. The work describes pre-processing
techniques for detecting food attributes to improve image quality before segmentation. It offers
various segmentation techniques based on thresholds, regions, gradients (edge detection), and
classifications segmentation. Techniques such as noise removal (using linear and median filters)
and contrast enhancement (through histogram scaling and equalization) are discussed. The
authors stress the fact that pre-processing steps are crucial for achieving accurate segmentation
results in food quality evaluation and that different types of food images may require different
segmentation techniques for optimal results. In our paper we focus on computer vision tech-
niques that can be potentially taken with low-cost devices, eventually with a smartphone and
integrate this with blokcchain and smart contracts.
2.3. Blockchain in Supply Chain Management
After exploring the importance and capabilities of image processing in data visualization, the
focus now shifts to another critical aspect of modern technology: blockchain in supply chain
management. A recent contribution to this field is evident in the paper ”A Blockchain-Based
System for Agri-Food Supply Chain Traceability Management” by [10], which introduces a
comprehensive model for a blockchain-based agri-food supply chain traceability system. While
image processing enhances the ability to analyze and interpret visual data, blockchain technology
offers a decentralized and transparent framework for tracking and verifying the authenticity
and integrity of products as they move through the supply chain. Embracing Hyperledger Fabric
as its framework, the proposed system leverages a permissioned blockchain, offering a scalable
and distributed solution tailored to the specific needs of the agri-food industry. The system
uses smart contracts to automate supply chain operations, allowing for seamless registration of
product types, batch creation, and ownership transfers among participating organizations. The
integration of rule-based mechanisms further enhances the system’s capabilities, enabling the
implementation of quality control measures to ensure adherence to predefined parameters. The
significance of this paper lies in its practical use cases, illustrating the successful automation
of supply chain management and the maintenance of transparent and immutable traceability
information. The presented scenarios, ranging from ideal operations to alternative pathways
with challenges, underscore the adaptability and robustness of the proposed model. As a
valuable addition to the literature on blockchain applications in agri-food supply chains, this
paper lays the groundwork for more sophisticated rule engines and performance evaluations,
signalling a promising trajectory for future advancements in the field.
The literature review highlights the diverse range of research efforts to address challenges in
saffron quality assessment, spanning from innovative approaches in image processing to utilising
blockchain technology for supply chain integrity. These studies underscore the multidisciplinary
nature of saffron quality research and the significance of technological advancements in ensuring
the authenticity and quality of this prized spice.
3. Saffron cultivation, classification and market
In this section, we aim to delve into the various facets of saffron production, distribution, and
classifications. We also aim to expound upon the predominant global locations where saffron
cultivation is prolific. Furthermore, our discourse endeavours to elucidate the diverse categories
of saffron and shed light on the key stakeholders within the saffron supply chain.
Saffron, known as ”the red gold”, is a valuable plant and one of the most expensive cash crops
worldwide [11]. With a history spanning over 4000 years, it has traditionally been used for its
tonic and antidepressant qualities in medicine [12]. Derived from the dried red stigmas of the
Crocus sativus L. flower, saffron is not only used to enhance the colour, flavour, and aroma of
various dishes such as ’paella’ in Spain, ’Milanese risotto’ in Italy, and ’Lussekatter’ buns in
Sweden, it is also gaining popularity in the food industry for its health benefits. In the modern
context, saffron’s inclusion in diets is on the rise, driven by its perceived positive impact on
human health, making it particularly appealing to consumers [13].
The world’s leading saffron producers include Iran, Greece, Morocco, Spain, and Italy. Addi-
tionally, since 2015, Afghanistan, a neighbouring country to Iran, has also become involved in
this market [14]. India also contributes to global saffron production, accounting for approxi-
mately 5% of the total output. Most of India’s saffron production, about 90%, is concentrated in
the Jammu and Kashmir region [15]. Experts in Iran classify saffron into three main categories:
Sargol, Pushal, and Negin. This categorization is significant as it denotes distinct grades of
saffron. Sargol, for instance, represents the top portion of the red filament and is characterized
by a high concentration of crocin compounds, which contribute to saffron’s distinctive colour
[16]. Crocin, functioning as the primary carotenoid, is accountable for the saffron colour. Picro-
crocin, conversely, is the compound responsible for the bitter taste associated with saffron. As
for Safranal, it is among the many molecules contributing to the distinctive aroma of saffron
[17].
Appreciating the differences among various saffron types is essential for its effective utilization
across diverse fields such as medicine, culinary, and textile production.
The proliferation of adulteration on saffron or altered saffron poses a considerable challenge,
Cultivated Area of Saffron (2010-2016)
100
Cultivated Area (ha 1000)
80
60
40
20
0
2010 2012 2014 2016
Year
Figure 2: Saffron Cultivated from 2010 to 2016 [12].
Figure 3: Saffron Sargol grade (a), Saffron Negin grade (b), Saffron Pushal grade (c)
as it undermines the integrity of the saffron market. Issues such as adulteration and mislabeling
compromise the quality of saffron products and erode consumer trust. Detecting and addressing
these fraudulent practices are crucial not only for safeguarding the economic interests of the
saffron industry but also for ensuring consumers receive genuine, high-quality saffron with its
intended health benefits and culinary attributes.
Kashmiri saffron is encountering sales challenges due to the absence of standardization,
certification, and quality assurance. Additionally, a lack of analysis and development further
compounds the issues. Adulteration emerges as a significant concern, contributing to a decline
in saffron demand and subsequently leading to a deterioration in its overall quality [3]. Based
on a study conducted on saffron available in the Indian market, findings reveal that only 52%
of the saffron samples are authentic, while 30% are of poor quality, and 17% are adulterated.
This alarming trend of saffron adulteration is rapidly growing, presenting itself as a significant
challenge and a form of white-collar fraud [18].
While accurate, traditional laboratory methods for detecting saffron adulteration are often
inaccessible to many due to their complexity and the need for specialized equipment. Conse-
quently, there’s a growing necessity for more user-friendly, cost-effective methods that can be
utilized outside laboratory settings. These methods aim to empower consumers and small-scale
vendors to verify the authenticity of saffron using a camera cellphone or other functional daily
tools, ensuring quality and safety in the market. The development of such methods is critical in
combating the widespread issue of food fraud, specifically in the saffron industry, where the
stakes are exceptionally high due to the spice’s esteemed status and economic value. Indeed,
crocin, safranal, and picrocrocin are vital components that determine saffron quality. Global
standards stipulate that these compounds, along with saffron’s appearance, dictate its overall
quality [19]. This article aims to empower users to discern potential saffron fraud by addressing
these components.
Various factors can influence the quality of saffron, and temperature is one of them. Several
research studies have proven that heat has many negative effects on the vital components of
saffron, such as crocin and safranal. Exposure to normal to high temperatures, especially those
exceeding 60 degrees Celsius, can degrade crocin content and destroy other vital components
of saffron [20, 21].
Figure 4: Normal saffron filaments (a), Pressing and heating the saffron filaments with steam (b),
manipulated saffron with no nutritional value (c).
One prevalent form of saffron fraud, often referred to in the saffron market as ”Indian saffron”
or ”ironed saffron,” involves using a steam press machine. This method involves pressing saffron
strands between two heated fabric plates, exposing saffron to steam. The goal is to straighten
the saffron filaments and increase their length. However, this fraudulent practice damages the
plant tissue and alters the saffron’s appearance characteristics, which are crucial determinants
of its quality. Furthermore, the process results in the loss of essential compounds such as crocin,
safranal, and picrocrocin, rendering the final product devoid of nutritional value.
4. Blockchain for saffron quality and sustainability
The potential of blockchain technology to enhance quality control in the agri-food supply chain
is increasingly recognized. Its immutable and decentralized ledger system offers unparalleled
traceability and transparency, addressing key challenges within the industry. By tracking
agricultural products from farm to fork, blockchain ensures each process step is documented
and verifiable. This level of traceability is crucial for verifying the authenticity of food items,
enabling real-time monitoring of manufacturing, processing, and delivery phases.
The current challenges in agri-food quality assurance, such as adulteration and lack of
traceability, are significant hurdles to ensuring food safety and consumer trust. Adulteration, the
deliberate addition of inferior materials, is a common issue in the food industry, compromising
the quality and safety of products. Similarly, the lack of a transparent and traceable supply chain
makes it difficult to pinpoint the origin and handling of food products, leading to potential health
risks and loss of consumer confidence. Blockchain technology addresses these challenges by
providing a secure and transparent way to record and verify each transaction within the supply
chain. The supply chain evolves into a more interconnected framework in a blockchain-enabled
system. Every transaction recorded on the blockchain becomes accessible to every member
within the network. This transparency improves the ability to track products, fostering greater
confidence in the entire process [22].
Blockchain technology offers a multitude of solutions to enhance traceability and transparency
in the saffron supply chain, as well as in other food product supply chains. One of these solutions
is MultiChain platform or Hyperledger Fabric, the use of MultiChain platform, a permissioned
blockchain platform, is crucial. It creates a secure and controlled environment, ideal for scenarios
involving a network of known and trusted organizations. This aspect of the system ensures
that sensitive supply chain data is managed securely, maintaining the integrity of information
across various stakeholders.
Additionally, using smart contracts to automate supply chain operations represents a signifi-
cant innovation. The smart contracts, executed within the blockchain, automate transactions
and processes, enhancing the efficiency and reliability of the supply chain management. This
automation is particularly effective in reducing human error and increasing the speed of oper-
ations, providing a robust mechanism for handling complex supply chain workflows. In the
saffron supply chain, where consumers may lack the necessary information to recognize the
qualities and distinguish between different types of saffron, the use of automatic systems such
as IoT sensors or computer vision helps to record information with complete details accurately.
Such validation ensures that the products adhere to the regulatory requirements and quality
standards, thus enhancing the food supply chain’s overall quality control and safety.
These elements collectively contribute to creating a more efficient, secure, and reliable agri-
food supply chain management system, representing significant advancements in leveraging
blockchain technology for enhancing traceability and quality assurance in the food industry.
Farmer Farmer
Records cultivation data
IOT Sensors IOT Sensors
Sends data to blockchain
Blockchain Blockchain
Platform Platform
Immutable & Transparent
Stores data security
Ledger
MultiChain MultiChain
Platform Platform
Ttriggers smart contract
Smart Smart
Contract Contract
product history
Automates Transactions VVerifies product quality
Accesses
Quality Quality
Control Control
Ensures Standards & Safety Approves and forwards products
Distributor Distributor
Distributes verified products
Retailer Retailer
Sells to customers
Customer Customer
Figure 5: Actors of the saffron supply chain and blockchain platform.
5. Digital Technologies
Computer vision technology offers multifaceted applications, ranging from noise reduction and
object removal in images to facial recognition and database searches for identifying individuals.
This advanced technology is crucial in mitigating human diagnostic errors, particularly in
contexts such as saffron analysis, where intricate details within each filament must be accurately
assessed and differentiated. These advancements in image processing algorithms, particularly
when combined with tools like OpenCV and MATLAB, offer a framework for the classification
and grading of saffron. The proposed algorithms can discern subtle differences in saffron
samples, aiding in quality control and fraud detection in the saffron industry.
Image processing involves defining a two-dimensional function, 𝑓 (𝑥, 𝑦), where 𝑥 and 𝑦
represent spatial coordinates, and the amplitude of 𝑓 at any pair (𝑥, 𝑦) is the image intensity or
grey level. When 𝑥, 𝑦, and amplitude values are finite and discrete, the image is termed a digital
image. Digital image processing, carried out by a computer, involves manipulating these finite
elements. Computer vision, a branch of artificial intelligence and image processing, focuses
on computer interpretation of real-world images. It combines low-level image processing (e.g.,
noise removal, contrast enhancement) with higher-level pattern recognition to identify image
features [5].
In the field of agri-food technology, the creation of image data processing systems is an
important advancement. These systems greatly lessen the need for laborious and possibly
arbitrary manual inspection by automating the analysis process. They can accurately and
reliably identify and categorise characteristics like saffron appearance differences and fraudulent
product manipulations. This improves agricultural productivity and sustainability by enabling
Measurement Filaments and calssified distribution in 3 types of saffron
Calculate the Remove Examining the make PDF and CDF for
consider Image Image Image
length oversize results for 3 each types of saffron
3 types Noise convert to edge
and width of and unuseful quality groups of to show distribution of
of saffron Elimination binary image detection
filaments data saffron filament length
Data input Data output
Collection Pictures Collection Collection
of Saffron
checking the curvature of saffron filaments to detect the counterfeit of pressed saffron
Receive data Calculate the Save the Creating a classification
Checking the Statistical check of the Making a final
from the width ratio degree of by examining the
length and degree of probability
Image Processing previous the length of curvature of saffron threshold diagnosis
width of the curvature
process filaments strings in each type of saffron
Data input Data output
Collection Collection
Checking the color of saffron filaments to determine the quality
Image Calculate the
consider Image Image detect red give result and make
convert to detect yellow percentage of
3 types Noise segmentation color a graph for percentage
identify color color percentage each color compared
of saffron Elimination detection percentage of red and yellow color
distinctions to each other
Data input Data output
Collection Pictures Collection Collection
of Saffron
Figure 6: Image processing
real-time monitoring and decision-making and ensuring more objective results.
The development of mobile or portable devices utilizing image processing technology for
on-the-spot quality assessment of saffron represents a significant advancement. This technology
empowers vendors and buyers in markets by providing immediate, accurate assessments, reduc-
ing the risk of adulteration, ensuring fair pricing, and offering the opportunity to identify real
quality saffron in a user-oriented way without the need for laboratory equipment. Additionally,
it facilitates recording information throughout the saffron supply chain by all stakeholders in-
volved. Furthermore, integrating such technology in the saffron market streamlines the quality
assurance process and enables the submission of data gathered from these algorithms onto the
blockchain network. This integration ensures the retention and preservation of accurate data
on a transparent network.
6. Methodology and Data
This research introduces an innovative methodology for the computational analysis of saffron
filament curvature, diverging from traditional quality assessment methods. The approach
combines computer vision’s precision with MATLAB and OpenCV’s analytical power to offer a
groundbreaking way of evaluating saffron quality through its filament curvature. A compre-
hensive dataset of saffron filament images is compiled from various authentic sources, ensuring
a wide range of curvature variations. These images are meticulously selected to represent
different grades and conditions of saffron, providing a robust foundation for the analysis.
The images undergo a sophisticated processing pipeline, which utilizes advanced filtering
and segmentation techniques; the individual filaments are isolated from the image background,
focusing on their unique curvature attributes. A custom-built algorithm is developed to analyze
and quantify the curvature of the saffron filaments. This algorithm leverages the capabilities of
(a) (b)
Figure 7: The adulterated saffron after heat and to sift (a), the normal saffron as same quality before
heat (b)
MATLAB and OpenCV to detect subtle curvature differences for quality assessment. The curva-
ture data extracted from the algorithm undergoes statistical analysis to establish correlations
between filament curvature and saffron quality to detect normal saffron without fraud. This
analysis aims to identify patterns and thresholds that can be used to identify saffron objectively.
6.1. Data Collection
For the initial development of the algorithm and image processing, standard saffron images
were captured in a laboratory using common mobile cameras with typical noise levels. This
approach was chosen to make the algorithm easily usable for everyday users. During this data
collection phase, specific guidelines and methods were established, leading to the following
steps:
1. Photos of adulterated saffron, high-quality saffron and lower-grade saffron were taken to
form the first set of samples. In this set, the saffron with the longest filaments represented
the highest quality and the shortest had the lowest quality.
2. The saffron was laid out on a white sheet of paper, and the camera was positioned 20 cm
away from the plate.
3. Each saffron sample weighed exactly one gram.
4. To capture diverse images, each identical sample was photographed ten times. The paper
was shaken between each shot to rearrange the saffron strands in various orientations.
5. The same saffron filaments were then crushed to simulate low-quality saffron for addi-
tional photography, forming the second set of samples. However, the adulterated saffron
samples were left as is for this process.
6.2. Analysis of Filament Size
This section focuses on a technique to enhance the visibility of saffron filaments in images.
The method begins by converting images to grayscale and creating a binary representation
to differentiate between black and white areas. The process then involves measuring the
length and thickness of the saffron filaments. Specific criteria are used to select filaments
for analysis, and their lengths are plotted in histograms. This analysis is further applied to
saffron samples of different quality levels, allowing for an evaluation of saffron quality. The
Cumulative Distribution Function (CDF) is employed in this script to draw comparisons in
saffron quality across various categories. This part of the study delves into three distinct quality
categories of saffron: ’A’, ’B’, and ’C’. For each category, the research meticulously examined ten
images of saffron filaments. A key analysis component was using data visualization techniques,
particularly histograms, to illustrate the distribution of filament lengths across each quality
category. Cumulative Distribution Functions (CDFs) provided additional insight into the range
and variability of filament lengths.
The methodology also entailed calculating and visually representing the proportions of red
and yellow colours in the saffron filaments, which served as an objective indicator of colour
composition. The colour composition of saffron filaments is crucial in determining their potency
and general quality, so it is evaluated with special attention. Notably, one of the important
factors in detecting saffron quality is that higher-quality saffron filaments are frequently linked
to a richer red colour. This relationship arises from the carotenoid pigment crocin, which
gives saffron its characteristic red colour and directly correlates with strength and quality.
Conversely, yellow segments in saffron filaments, often found towards the ends of the strands,
indicate lower quality [6]. Bright red indicates a high crocin concentration, which indicates
superior colouring power for high-quality saffron. Furthermore, the analysis involved isolating
objects based on their diameters to understand the correlation between size and quality. This
classification enabled an exploration of filament length distributions within each size class, using
histograms and CDFs to assess cumulative probability trends and the proportion of filaments
within specific length ranges. After the initial inspection and grading of saffron quality, the
curvature degrees of saffron filaments are calculated using the previous data of the filaments’
length and diameter by creating a mathematical ratio of width to length. Finally, the algorithm
identifies the filaments of each group of saffron as saffron adulteration if the curvature value is
less than normal.
7. Final Remarks and Future Directions
Blockchain application, developed on the MultiChain platform, streamlines the management of
the saffron supply chain. Leveraging Bitcoin’s underlying technology, MultiChain enables swift
blockchain application development. It organizes data in distinct streams based on user and year,
each containing user-generated data blocks or transactions. The application offers three types
of data streams - public, business, and reserved - tailored to different privacy needs. User data
is distributed across multiple blockchain nodes to enhance stability and security. Additionally,
users can utilize dedicated nodes, ensuring data redundancy. A notable aspect of our system
is the use of ”smart filters,” which serve as a more efficient alternative to conventional smart
contracts. These filters assess data before it’s added to the blockchain, deciding whether to log
it or tag it with special identifiers. For example, saffron meeting certain criteria can be tagged
as ”organic.” Smart filters also validate existing data when added to the blockchain, allowing
1 1
0.9 0.9
0.8 0.8
Cumulative Distribution
Cumulative Distribution
0.7 0.7
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
High Quality
High Quality
Middle Quality
0.1 Middle Quality 0.1
Low Quality
Low Quality
0 0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0 0.1 0.2 0.3 0.4 0.5
Thickness-to-Length Ratio (Curvature) Thickness-to-Length Ratio
(a) (b)
Figure 8: CDF of ratio width to length three types as each group (a), CDF of ratio width to length thirty
pictures of three types saffron (b)
1 1
0.9 0.9
0.8 0.8
Cumulative Distribution
Cumulative Distribution
0.7 0.7
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 Type C 0.2
Type B Short Filaments
Type A Middle Filaments
0.1 0.1
Long Filaments
0 0
0 50 100 150 200 250 0 50 100 150 200 250
Filaments Length [mm] Filament Length [mm]
(a) (b)
Figure 9: CDF of three types of saffron and their intersection with a pre-defined threshold (a), CDF of
length thirty pictures of three types saffron (b)
for ongoing quality checks. All stakeholders in the saffron supply chain, including farmers,
producers, sellers, and buyers, contribute data to the blockchain. This collaborative approach
creates a detailed product history, ensuring complete traceability. In the specific case of the
saffron supply chain, smart filters evaluate the length and colour of saffron strands through
Algorithm 1 Algorithm for detecting saffron adulteration
ℎ𝑒 ← rgb2gray(image) ▷ Convert RGB image to grayscale
𝑏𝑤 ← im2bw(image) ▷ Convert image to binary image
𝑏𝑤 ←∼ 𝑏𝑤 ▷ Invert binary image
𝑠𝑡𝑎𝑡𝑠 ← regionprops(binary_image) ▷ Find contours and shape properties
𝑐𝑐 ← bwconncomp(𝑏𝑤) ▷ Find and count connected components
[𝑜𝑢𝑡𝑀𝑎𝑥, 𝐿𝑀] ← Feret(𝑐𝑐) ▷ Get shape stats and filter out outlayers
𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑙𝑒𝑛 ← outMax.MaxDiameter(1 ∶ maxLabel) ▷ Get maximum diameters for each
filament
𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑡ℎ𝑖𝑐𝑘 ← outMin.MinDiameter(1 ∶ maxLabel) ▷ Get minimum diameters for each
filament
[𝑜𝑢𝑡𝑀𝑖𝑛, 𝐿𝑀] ← Feret(𝑐𝑐) ▷ Compute Feret properties
𝑣𝑎𝑙𝑖𝑑𝑝𝑜𝑠𝑙𝑒𝑛 ← find(𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑙𝑒𝑛) ▷ Find lengths of valid filament
𝑣𝑎𝑙𝑖𝑑𝑝𝑜𝑠𝑡ℎ𝑖𝑐𝑘 ← find(𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑡ℎ𝑖𝑐𝑘) ▷ Find thicknesses of valid filament
𝑣𝑎𝑙𝑖𝑑𝑝𝑜𝑠𝑡ℎ𝑖𝑐𝑘 ← find(𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑡ℎ𝑖𝑐𝑘) ▷ Calculate width to length ratio on each
𝑣𝑎𝑙𝑖𝑑𝑝𝑜𝑠𝑡ℎ𝑖𝑐𝑘 ← find(𝑓 𝑖𝑙𝑎𝑚𝑒𝑛𝑡_𝑡ℎ𝑖𝑐𝑘) ▷ Classified the saffron normal and abnormal according
threshold
image analysis and assign quality marks based on predefined standards.
In this research, we used the Multichain platform to evaluate the methodology and feasibility
of traceability and quality verification in the saffron supply chain. The blockchain can efficiently
handle the requested transaction volumes, the number of which depends on the statistical
compression of the row data. Time to run the CV algorithm makes writing on the blockchain
processing time, as the requirements are not strict. We consider one image per second of
the conveyor belt and then analyze 60 images; this implies one writing on the blockchain
per minute, showcasing its scalability and robustness. These performance metrics fit the
blockchain’s capability to provide real-time traceability and immutable record-keeping in a
high-demand market like saffron.
Our blockchain implementation uses MultiChain technology, which is tailored to facilitate
the secure and efficient management of the saffron supply chain by integrating its data ingestion
with the capability of Computer Vision. Our system’s architecture is built around a tailored
consensus algorithm through a smart filter derived from MultiChain’s inherent multilateral
mining approach. This consensus model is engineered to distribute control equitably among
all participating nodes, ensuring no single entity can dominate the decision-making process.
Additionally, it enhances the robustness of our blockchain-based platform by preventing any
single point of failure, which is crucial for maintaining continuous operations through the
saffron supply chain. This setup ensures real-time transaction verification and consistent
updates across all nodes. Furthermore, we have developed specific smart filters to enhance the
traceability and the automation level of the saffron quality verification. These smart contracts
streamline operations such as batch tracking, quality certification, and payment processes,
enhancing the saffron market’s operational efficiency and transparency.
7.1. Create and deploy Redgold Blockchain on Multichain
The initial step in establishing the Redgold blockchain involved creating and deploying the
first node onto the network. For this task, we utilized MultiChain and incorporated various
stakeholders of the saffron supply chain, ranging from farmers to customers. This process
included installing MultiChain on the primary node and configuring the genesis block, which
serves as the foundation of the blockchain. After the creation, the Redgold blockchain was
deployed by starting the blockchain on the primary node, we created other permissions and
rules on the network. Additional nodes were connected to the Redgold blockchain network
by installing Multichain on other machines and using the connection information provided
by the primary node. This step is crucial for creating a decentralized network where multiple
participants can interact with the blockchain. For each participant in the supply chain, a unique
blockchain address was generated. These addresses represent farmers, logistics providers,
quality control labs, distributors, retailers, and customers.
Streams were created on the Redgold blockchain to represent categories of data and trans-
actions relevant to the supply chain. Each stream was assigned to an aspect of the supply
chain, such as production data for farmers to submit all data from the producing step, like
the Origin area of the product, and other streams like logistics, quality control, distribution,
retail, and customer feedback. Permissions were allocated to each participant, allowing them
specific interactions with the streams. The permissions include writing, reading, and sending
transactions within assigned streams. This granularity in permission settings ensures data
integrity and access control within the blockchain network, as explained in Algorithm 2.
8. Conclusion
This research provides a methodological approach to saffron quality assessment, blending
computer vision’s objectivity with blockchain technology’s transparency. Our study demon-
strates the effectiveness of using a quality metric and algorithm for analyzing the curvature of
saffron filaments in quality assessment to detect counterfeit saffron. This method addresses
a significant gap in quality control practices, predominantly focused on colour, aroma, and
chemical composition.
By introducing a reliable and accessible way to evaluate saffron quality, this approach em-
powers producers, suppliers, and consumers to ensure the authenticity and purity of saffron.
Integrating blockchain technology into the saffron supply chain is a pivotal advancement in
the digital domain but still requires special attention to guarantee correspondence with the
physical one. In addition to using decentralized and secure digital platforms, we suggest to use
computer vision to monitor saffron during the whole supply chain, requiring a methodology
that automatically extract info from filaments and compare them over time. Future research
should focus on the potential of the proposed integration of blockchain and computer vision
also to use visual features as markers that fingerprint the product; in order to identify it beyond
classification.
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Algorithm 2 Blockchain Setup for Saffron Supply chain
Initialization:
Create blockchain named Redgold
Start Redgold in daemon mode
Announce node connection details
Address Generation:
for each participant in {Farmer, Logistic, Lab, Distributer, Retailer, Customers} do
Farmer: 1JcTSXmswoyBrZmdx4HJESPZFKk6uJTDD8w8iV
Logistic: 1aSRzNR2QtfsYeFote84Tw3pu3oTY7maGcDq27
Lab for check and test quality: 1FqK5h47Xu2JyWJj6sme5WxaBQbxT57JZNGrUb
Computer Vision (Quality check center): 1VEEk1anPgun4wPzYFhVEHpKRBnDgZ8BjrU8f3
Distributer: 1NM3ecdYadQjZmKipwoRM5i4XwNFvig1TtZyzB
Retailer: 1AEELHd9uUsQMSFhPtrPkF5xcDHRFGzjYvqnZ4
Customers: 1WorygsvwuVxGAubXgPpJF2ECQP54SCZxgWG8k
end for
Stream Creation:
for each stream in {Qualitycontrol1, Distributer1, Retail1, Customers1, Logistic1} do
Create stream with write restriction:
{Productiondata1: 04a9f5526f5ed0dbb6edb23048b9dde50288d347987e7be52398758aecbfc368}
{Logistic1: 02bff25f6647242c166257a1249e5a537c0a2bc7fd4a6b987054e5567fa41ad1}
{Quality Control1: acdf8a4d3e518e03c4f61c58b7b5ad137d5a0537bb206cc7dddfbaca0bc4045a}
{Distributer1: 6937ee4c5441936386d3aacbab766ae87c04a09e542fcf9af0f48ce4b3b110b4}
{Retail1: 934a518e178e222788d1b159de5b90a9f1aae3b3d0cc4c4aa9ed08ca633f48bd}
{Customers1: ff91920028a8b0175f0c86eda185e68075ab2aa455c69e7366187ad28e5907a6}
end for
Permission Granting:
for each participant and their respective stream do
Grant write permission to participant for their stream
Grant read permission to participant for their stream
Grant send permission to participant
Access:
Farmer
Write: 47890a61d5644753886ed0fa776ba8ab8aac9491559831ecbe2a8c926ab02ca8
Read: b57f88b70a53c61b73dbc74686b2537b04da8d7325e0c9bec20997885f6dee8f
Send: 76654bdffdf6f3df036db9ab9c78ff87810ce93ebc06b9194deae1ce87819cb2
Logistic:
Write: 7c521f520723d7501835b16f3e747e497d1074a4205e52328366fe337c210a56
Read: 8c695175fbda7bd6d6cb280ebf755d1e35b611969fdb7450a608666757e07a42
Send: cf9b34178f507ff62b361e1b366b5ed93d5f75191bb770bccd7ffb29b4627284
Quality Control:
Write: 4ea81ebe2d4672697ae32748a89d0f2e088b67871fd3ee32d78d7fdc23911fb9
Read: bd3d90166df0a5dec4e4d34d01e3f8a3543e20f6fb55ead071356804e2d983be
Send: 9df5ecc83a0e5ab6a46b80e86ef43f61e6808ece581dae18bc0bc35f89a4ae39
Distributer:
Write: 16b7b3fd4b2da18fc199ed799aad7052a0a570592e5bedabce44b64e7b80f52c
Read: 141b8982b4727245115e5749e0a286464a221329334c2804790d138a9be013d6
Send: 6d12ce82298497ca0cc1ad0966aaa99df9b28fae08f588f9fa7f6b100bfce4c8
Retailer:
Write: 0932e6921806b52403574a1e1d2dbae7872a2e9172fddd57ece7f5870c0c8793
Read: 466c83b1d48e6cabad41eb85a36eddc32b0eb74104223f17f44d66081d8a5427
Send: 895a4150fe54cd8363cfa1ded2e04c81c5f50cad3bebd30c6b05906e12c5b13e
Customer:
Write: 13d1907cb471a00a59a8659787d36853178faaabcebd810945b4d8ff01d36f7c
Read: 21655b7ab10c8d1e3cd77b07ac7e5d9e220a5c18f2e7207a7030b6d9d0e8ced5
Send: 1d8ad32e90cafc4ec4a8b1655978156ab26bfdf31aec464d8fa43717039c3638
end for