Utilizing Blockchain Technology and Machine Learning for Quality Evaluation in Agricultural Supply Chains

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.