In this paper, we present a study on the curvature analysis of safron filaments using computer vision. The goal is to develop a reliable method for detecting altered safron by analyzing the curvature of safron iflaments in captured pictures using image processing. Safron can be altered through processes such as pressing and ironing, one of the usual frauds on the safron supply chain. We explore the integration of blockchain technology in the safron supply chain to enhance traceability and ensure economic and societal sustainability.
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Safron, 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 safron involve intricate methods, and the quality of safron is often determined
by the characteristics of its filaments [
Additionally, post-harvest, a dehydration treatment is essential to transform the stigmas of
Crocus sativus L. into safron spice. This dehydration process results in the stigmas losing
approximately 80% of their original weight [
Another reason for the high price and value of safron 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
of safron in the world, collectively responsible for around 90% of global production, equivalent
to approximately 300 tons annually [
The majority of safron production in India is concentrated in the Jammu and Kashmir region. Currently, Kashmiri safron 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 safron 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 safron. The fundamental concept involves endowing machines with the
ability to recognize what is apparent to humans, particularly in inspecting safron filaments
and distinguishing its color, length, and straightness. The objective entails instructing
computational systems to visually perceive and interpret images or videos akin to an experienced
specialist, albeit with the precision and steadfast attention characteristic of algorithms [
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 ofers a transparent, traceable, and decentralized ledger system that is tamperresistant by design. This secure framework holds particular promise for enhancing transparency and ensuring the traceability of high-value goods such as safron. Products like safron are susceptible to counterfeiting, both in terms of physical products and associated data throughout the safron supply chain. By integrating blockchain into the safron 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 decisionmaking 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.
A diverse range of research and innovation fields ofers a wealth of novel perspectives, sophisticated techniques, and cutting-edge technological developments. This section explores the
Network collecting information from farm to fork reduced human errors, interferences and frauds Sustainability Traceability and Transparency
features of saffron strands current body of knowledge on safron 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.
This paper extends a previous work on computer vision and blockchain for safron in [
In [
The work in [
The research on the relationship between computer vision and food quality assessment is particularly relevant to our analysis.
In [
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 [
The literature review highlights the diverse range of research eforts to address challenges in safron quality assessment, spanning from innovative approaches in image processing to utilising blockchain technology for supply chain integrity. These studies underscore the multidisciplinary nature of safron quality research and the significance of technological advancements in ensuring the authenticity and quality of this prized spice.
In this section, we aim to delve into the various facets of safron production, distribution, and classifications. We also aim to expound upon the predominant global locations where safron cultivation is prolific. Furthermore, our discourse endeavours to elucidate the diverse categories of safron and shed light on the key stakeholders within the safron supply chain.
Safron, known as ”the red gold”, is a valuable plant and one of the most expensive cash crops
worldwide [
The world’s leading safron producers include Iran, Greece, Morocco, Spain, and Italy. Additionally, since 2015, Afghanistan, a neighbouring country to Iran, has also become involved in this market [14]. India also contributes to global safron production, accounting for approximately 5% of the total output. Most of India’s safron production, about 90%, is concentrated in the Jammu and Kashmir region [15]. Experts in Iran classify safron into three main categories: Sargol, Pushal, and Negin. This categorization is significant as it denotes distinct grades of safron. Sargol, for instance, represents the top portion of the red filament and is characterized by a high concentration of crocin compounds, which contribute to safron’s distinctive colour [16]. Crocin, functioning as the primary carotenoid, is accountable for the safron colour. Picrocrocin, conversely, is the compound responsible for the bitter taste associated with safron. As for Safranal, it is among the many molecules contributing to the distinctive aroma of safron [17].
Appreciating the diferences among various safron types is essential for its efective utilization across diverse fields such as medicine, culinary, and textile production.
The proliferation of adulteration on safron or altered safron poses a considerable challenge,
Year as it undermines the integrity of the safron market. Issues such as adulteration and mislabeling compromise the quality of safron products and erode consumer trust. Detecting and addressing these fraudulent practices are crucial not only for safeguarding the economic interests of the safron industry but also for ensuring consumers receive genuine, high-quality safron with its intended health benefits and culinary attributes.
Kashmiri safron 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 safron demand and subsequently leading to a deterioration in its overall quality [
While accurate, traditional laboratory methods for detecting safron adulteration are often inaccessible to many due to their complexity and the need for specialized equipment. Consequently, there’s a growing necessity for more user-friendly, cost-efective methods that can be utilized outside laboratory settings. These methods aim to empower consumers and small-scale vendors to verify the authenticity of safron 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 safron 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 safron quality. Global standards stipulate that these compounds, along with safron’s appearance, dictate its overall quality [19]. This article aims to empower users to discern potential safron fraud by addressing these components.
Various factors can influence the quality of safron, and temperature is one of them. Several research studies have proven that heat has many negative efects on the vital components of safron, 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 safron [ 20, 21].
One prevalent form of safron fraud, often referred to in the safron market as ”Indian safron” or ”ironed safron,” involves using a steam press machine. This method involves pressing safron strands between two heated fabric plates, exposing safron to steam. The goal is to straighten the safron filaments and increase their length. However, this fraudulent practice damages the plant tissue and alters the safron’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.
The potential of blockchain technology to enhance quality control in the agri-food supply chain is increasingly recognized. Its immutable and decentralized ledger system ofers 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 dificult 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 ofers a multitude of solutions to enhance traceability and transparency in the safron 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 significant innovation. The smart contracts, executed within the blockchain, automate transactions and processes, enhancing the eficiency and reliability of the supply chain management. This automation is particularly efective in reducing human error and increasing the speed of operations, providing a robust mechanism for handling complex supply chain workflows. In the safron supply chain, where consumers may lack the necessary information to recognize the qualities and distinguish between diferent types of safron, 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 eficient, secure, and reliable agrifood supply chain management system, representing significant advancements in leveraging blockchain technology for enhancing traceability and quality assurance in the food industry. IOT Sensors Blockchain Platform MultiChain Platform Smart Contract Quality Control Distributor Retailer Customer
Records cultivation data
Sends data to blockchain Immutable & Transparent
Ledger y sse itso Automates Transactions
r se th c c cA udo Ensures Standards & Safety r p
Stores data security
Ttriggers smart contract
VVerifies product quality
Approves and forwards products
Distributes verified products Sells to customers
Farmer IOT Sensors Blockchain Platform MultiChain Platform Smart Contract Quality Control Distributor Retailer Customer
Computer vision technology ofers 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 safron analysis, where intricate details within each filament must be accurately assessed and diferentiated. These advancements in image processing algorithms, particularly when combined with tools like OpenCV and MATLAB, ofer a framework for the classification and grading of safron. The proposed algorithms can discern subtle diferences in safron samples, aiding in quality control and fraud detection in the safron industry.
Image processing involves defining a two-dimensional function, (, ) , where and
represent spatial coordinates, and the amplitude of at any pair (, ) is the image intensity or
grey level. When , , 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 [
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 safron appearance diferences and fraudulent product manipulations. This improves agricultural productivity and sustainability by enabling Image Processing
Data input Colection Data input Colection Data input
Colection 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 safron represents a significant advancement. This technology empowers vendors and buyers in markets by providing immediate, accurate assessments, reducing the risk of adulteration, ensuring fair pricing, and ofering the opportunity to identify real quality safron in a user-oriented way without the need for laboratory equipment. Additionally, it facilitates recording information throughout the safron supply chain by all stakeholders involved. Furthermore, integrating such technology in the safron 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.
This research introduces an innovative methodology for the computational analysis of safron iflament curvature, diverging from traditional quality assessment methods. The approach combines computer vision’s precision with MATLAB and OpenCV’s analytical power to ofer a groundbreaking way of evaluating safron quality through its filament curvature. A comprehensive dataset of safron filament images is compiled from various authentic sources, ensuring a wide range of curvature variations. These images are meticulously selected to represent diferent grades and conditions of safron, 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 safron filaments. This algorithm leverages the capabilities of (a) (b) MATLAB and OpenCV to detect subtle curvature diferences for quality assessment. The curvature data extracted from the algorithm undergoes statistical analysis to establish correlations between filament curvature and safron quality to detect normal safron without fraud. This analysis aims to identify patterns and thresholds that can be used to identify safron objectively.
For the initial development of the algorithm and image processing, standard safron 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 safron, high-quality safron and lower-grade safron were taken to form the first set of samples. In this set, the safron with the longest filaments represented the highest quality and the shortest had the lowest quality. 2. The safron was laid out on a white sheet of paper, and the camera was positioned 20 cm away from the plate. 3. Each safron 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 safron strands in various orientations. 5. The same safron filaments were then crushed to simulate low-quality safron for additional photography, forming the second set of samples. However, the adulterated safron samples were left as is for this process.
This section focuses on a technique to enhance the visibility of safron filaments in images. The method begins by converting images to grayscale and creating a binary representation to diferentiate between black and white areas. The process then involves measuring the length and thickness of the safron filaments. Specific criteria are used to select filaments for analysis, and their lengths are plotted in histograms. This analysis is further applied to safron samples of diferent quality levels, allowing for an evaluation of safron quality. The Cumulative Distribution Function (CDF) is employed in this script to draw comparisons in safron quality across various categories. This part of the study delves into three distinct quality categories of safron: ’A’, ’B’, and ’C’. For each category, the research meticulously examined ten images of safron 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 safron filaments, which served as an objective indicator of colour
composition. The colour composition of safron 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 safron quality is that higher-quality safron filaments are frequently linked
to a richer red colour. This relationship arises from the carotenoid pigment crocin, which
gives safron its characteristic red colour and directly correlates with strength and quality.
Conversely, yellow segments in safron filaments, often found towards the ends of the strands,
indicate lower quality [
Blockchain application, developed on the MultiChain platform, streamlines the management of the safron 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 ofers three types of data streams - public, business, and reserved - tailored to diferent 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 eficient 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, safron meeting certain criteria can be tagged as ”organic.” Smart filters also validate existing data when added to the blockchain, allowing 0.9 0.8 for ongoing quality checks. All stakeholders in the safron 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 safron supply chain, smart filters evaluate the length and colour of safron strands through 50
100 150 Filaments Length [mm]
Type C Type B
Type A 200 250
Short Filaments Middle Filaments
Long Filaments 50
100 150 Filament Length [mm] 200 250 Algorithm 1 Algorithm for detecting safron adulteration
regionprops(binary_image) bwconncomp( ) ▷
Feret( ) ▷ _ ← outMax.MaxDiameter(1 ∶ maxLabel) ▷ Convert RGB image to grayscale ▷ Convert image to binary image
▷ Invert binary image ▷ Find contours and shape properties Find and count connected components Get shape stats and filter out outlayers
▷ Get maximum diameters for each outMin.MinDiameter(1 ∶ maxLabel) ▷ Get minimum diameters for each _) _ℎ) _ℎ) _ℎ) ▷
▷ Compute Feret properties ▷ Find lengths of valid filament ▷ Find thicknesses of valid filament ▷ Calculate width to length ratio on each
Classified the safron normal and abnormal according ℎ ← rgb2gray(image) ← im2bw(image) ←∼ ← ← [ , ] ← iflament _ℎ ← iflament [ , ] ← ← ℎ ← ℎ ← ℎ ← 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 safron supply chain. The blockchain can eficiently 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 safron.
Our blockchain implementation uses MultiChain technology, which is tailored to facilitate the secure and eficient management of the safron 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 safron 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 safron quality verification. These smart contracts streamline operations such as batch tracking, quality certification, and payment processes, enhancing the safron market’s operational eficiency and transparency.
The initial step in establishing the Redgold blockchain involved creating and deploying the ifrst node onto the network. For this task, we utilized MultiChain and incorporated various stakeholders of the safron 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 transactions 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.
This research provides a methodological approach to safron quality assessment, blending computer vision’s objectivity with blockchain technology’s transparency. Our study demonstrates the efectiveness of using a quality metric and algorithm for analyzing the curvature of safron filaments in quality assessment to detect counterfeit safron. 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 safron quality, this approach empowers producers, suppliers, and consumers to ensure the authenticity and purity of safron. Integrating blockchain technology into the safron 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 safron 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. [12] M. Shokrpour, Safron (crocus sativus l.) breeding: Opportunities and challenges, Advances in Plant Breeding Strategies: Industrial and Food Crops 6 (2019) 675–706. doi:10.1007/ 978-3-030-23265-8_17. [13] A. Kyriakoudi, S. A. Ordoudi, M. Roldan-Medina, M. Tsimidou, A functional spice, Austin
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