Intelligent Optoelectronic Sensing and IoT-Enabled Monitoring for Sustainable and Intelligent Food Processing and Process Automation

The field of food process engineering is undergoing a major transformation, driven by the need for stringent quality assurance, safety compliance, and sustainable practices in the global food industry. Emerging optoelectronic and photonic sensing technologies—such as hyperspectral imaging, NIR spectroscopy, Raman spectroscopy, and advanced computer vision—offer non-destructive, real-time ways to assess food quality, detect contaminants, and verify product authenticity. Recent years have also seen a surge in the adoption of Internet of Things (IoT) architectures, enabling intelligent, connected food processing environments through real-time environmental monitoring and automated control systems. However, despite rapid technological advances at the laboratory scale, challenges persist in the large-scale deployment and integration of these tools into complex industrial workflows, particularly for small- and medium-sized enterprises that form the backbone of regional food economies. Existing gaps revolve around robust sensor validation in operational settings, interoperability among diverse hardware platforms, and seamless integration with digital manufacturing ecosystems.This Research Topic aims to accelerate the development, validation, and industrial deployment of intelligent optoelectronic and photonic sensing solutions, interconnected through IoT platforms, to advance sustainable engineering in food processing systems. The main objective is to address the critical challenges at the intersection of sensor technology and food industry needs, including real-time, inline quality and safety assurance, smart sensor network design, and data-driven process automation. The collection aspires to foster multidisciplinary collaborations—spanning sensor science, food engineering, data science, and industry practice—to deliver transformative impact on food safety, supply chain transparency, and environmental stewardship. Central to this goal is the integration of AI and machine learning methods for enhanced data fusion, calibration, and decision support, creating scalable solutions that bridge laboratory innovation and industrial practice.The scope of this Research Topic is defined by the intersection of advanced optoelectronic and photonic sensing with IoT-enabled control specifically as applied to food processing operations. Submissions must demonstrate a clear sensing systems contribution within the context of food manufacturing and will not cover projects limited to general AI/ML theory, robotics engineering, or biosensors for clinical purposes.To gather further insights in this rapidly evolving field, we welcome articles addressing, but not limited to, the following themes:- AI-driven hyperspectral, NIR, Raman, and optical spectroscopy for real-time food quality and safety analysis- IoT-enabled smart sensor networks and monitoring infrastructures for food processing environments- Machine vision and sensor-based automation for inline inspection and control of food manufacturing processes- Intelligent sensing platforms enhancing traceability, regulatory compliance, and transparent supply chains- Sensing technologies facilitating sustainable byproduct valorization and waste reduction- Machine learning and multi-sensor fusion approaches for predictive analytics and calibration transfer Topic editors: Yiming Feng, Ozan Kahraman, Youngsoo Lee Source: https://www.frontiersin.org/research-topics/80085/intelligent-optoelectronic-sensing-and-iot-enabled-monitoring-for-sustainable-and-intelligent-food-processing-and-process-automation