Abstract

The study is concerned with the complexities of the coffee supply chain, which includes the stages of farming and the entire end-to-end retail process. It explores crucial areas such as cultivation practices, processing provisions, export-import logistics, roasting, packaging, distribution, and retailing. The difficulties and issues in each stage are analyzed, focusing on quality management, sustainability initiatives, logistical complexity, and market instability. Simultaneously, the paper employs machine learning algorithms, viz., linear Regression, kNN, and decision trees, to process the data related to coffee beans. Linear Regression is used to forecast market trends, consumer preferences, and price changes based on historical data. Market segmentation, consumer behavior prediction, and regional preferences in coffee drinking are analyzed using the kNN algorithm. In the meantime, decision trees are used to determine significant factors affecting coffee preference, market division, and predictive analytics. The data undergoes a stringent examination using the chosen machine learning algorithms to extract information about the complex trends and associations of the coffee industry. Findings from such analyses provide insight into the predictive power and usability of each machine learning method in addressing specific issues related to coffee bean data. The results provide helpful information on streamlining supply chain management practices, improving quality control mechanisms, and understanding consumer preferences in the dynamic coffee industry. The research highlights the significance of using machine learning methods to obtain practical insights from coffee-related data, supporting better management and decision-making in the supply chain. The core challenge in deploying such data-driven models across a multi-stakeholder supply chain spanning various Internet services lies in the integrity and privacy of shared commercial data. To address this critical concern, this paper proposes an extended framework that integrates two pivotal Internet Service and Security technologies. Firstly, we implement a lightweight Blockchain layer to ensure the integrity, immutability, and non-repudiation of key supply chain event data (e.g., harvest volume, quality scores) used as input for model training. Secondly, we introduce a Federated Learning (FL) approach to collaboratively train the demand forecasting model across decentralized regional data silos, thereby preserving the privacy of sensitive local information. This secure, distributed architecture demonstrates a novel path toward trustworthy and privacy-compliant data utilization in global agricultural supply chains, marking a significant contribution to the fields of Internet services and information security.