Abstract:
In modern agricultural supply chains,
ensuring the quality and authenticity of products is
crucial for maintaining consumer trust and maximizing
value. This paper proposes a novel approach that
integrates blockchain technology and machine learning
for quality evaluation in agricultural supply chains.
Blockchain technology offers a decentralized and
immutable ledger system, enabling transparent and
tamper-proof recording of transactions and product
information across the supply chain. By leveraging
blockchain, stakeholders can track the journey of
agricultural products from farm to table, including
information about cultivation practices, harvesting,
transportation, and storage conditions. Machine
learning algorithms are employed to analyze the vast
amount of data stored on the blockchain and identify
patterns related to product quality. These algorithms
can learn from historical data to predict potential
quality issues, such as contamination, spoilage, or
adulteration, and provide early warnings to
stakeholders. The proposed system enhances
transparency, traceability, and trust in agricultural
supply chains by enabling real-time monitoring and
verification of product quality. By identifying and
addressing quality issues promptly, stakeholders can
minimize losses, improve efficiency, and ultimately
deliver safer and higher-quality products to consumers.
Overall, the integration of blockchain technology and
machine learning offers a promising solution to enhance
quality evaluation in agricultural supply chains,
fostering greater accountability and sustainability
throughout the entire process.