Citrus Sorting Dynamic Control Using Multispectral Computer Vision

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.