Lack of digitization in data sharing between enterprises and inspection solutions suppliers negatively affects cash flows between parties, which results in late payments that negatively affect the adoption of automatic inspection equipment. This paper contributes to improving the implementation of a new Inspection as a Service Business Model for deploying automatic inspection solutions using non-destructive inspection solutions, and to enhance workflows by integrating Blockchain and Smart Contracts. The Inspection as a Service offers flexible, cloud-based, or on-premise inspection solutions through the Marketplace, reducing upfront costs with a recurring service fee and automated payments. The marketplace platform supports automatic payment processes and facilitates industry adoption of IaaS solutions. The digital ecosystem offers improved capital expenditure and payback periods. It enhances communication, collaboration, data sharing, and payment processes through a subscription model. The case study demonstrates that the IaaS Business Model (on-premise or cloud) improves the economic feasibility of automatic non-destructive inspection solutions by lowering initial investments and enhancing return on investment and payback periods, even with higher operating costs. The analysis confirms the profitability and sustainability of IaaS Business Model over traditional one-fee selling by emphasizing its potential to improve operational performance and sustainability in manufacturing. The current proposal of automatic non-destructive solutions implements a new revenue model based on pay-per-use or volume, which makes it more financially viable to adopt this technology in industry.

This paper presents the development of a dynamic control system designed to sort citrus fruits based on external defects identified through multispectral imaging. The proposed system combines PID controllers integrated through the OPC-UA protocol to facilitate real-time adjustments and optimize batch classification. By dynamically controlling classification thresholds, the system maximizes output within customer-specified tolerances, enhancing batch utilization and quality. Implementing Node-RED as an OPC-UA client enables seamless integration and real-time data processing, ensuring efficient and responsive control. Additionally, Docker containerization is employed to streamline deployment and scalability, enabling flexible and resource-efficient implementation across various operational environments. The system also introduces virtual classification tiers that dynamically adjust sorting thresholds, allowing for better utilization of each batch while maintaining adherence to strict quality specifications. Experimental evaluations demonstrate that the system significantly improves sorting quantities, optimizes classification efficiency, and reduces waste by adapting to varying conditions in real-time. These enhancements ensure high standards of quality and efficiency in citrus fruit classification while supporting sustainable agricultural and industrial practices through automated inspection.