Multi-Sensor Data Fusion and Analytics for Predictive Quality Evaluation of Fresh Produce

Dear Colleagues,

The quality and safety of agricultural products have become major challenges across the global food supply chain. Providing reliable and rapid quality assessment is essential to meet regulatory requirements and consumer expectations. Sensors, including optical systems (RGB, near-infrared, and hyperspectral imaging), physicochemical and electrochemical sensors, as well as olfactory (e-nose) and gustatory (e-tongue) devices, have emerged as powerful tools for data-driven agriculture. These technologies provide fast, accurate, non-destructive, and scalable solutions for characterizing the physical, chemical, and biological properties of agricultural commodities.

Recent advancements in machine learning (ML), deep learning (DL), and broader data-analysis frameworks have significantly enhanced sensor-based systems. These artificial intelligence algorithms have accelerated progress across many sensing domains, including e-noses, e-tongues, and spectroscopic and optical devices—many of which are increasingly integrated with computer vision methods. Together, these advances have enhanced sensing mechanisms and introduced sophisticated analytical approaches that are transforming how agricultural data is captured, interpreted, and applied in real-world field conditions for predictive quality assessment.

This Special Issue aims to present recent innovations in sensors and data-driven analytical methodologies for assessing the quality of fresh agricultural products. The proposal aligns with the journal’s scope by integrating engineering, agricultural technology, and intelligent systems applied to real-world problems in agriculture.

We welcome contributions that explore sensor design, hardware optimization, new signal-processing approaches, ML/DL model development, data-fusion strategies, and field applications. The scope is intentionally inclusive, yet focused, to encourage both methodological advances and applied studies.

In this Special Issue, original research articles and review papers are welcome. Research areas may include, but are not limited to, the following:

• Sensor design, development, and hardware optimization;
• Optical, spectroscopic, electrochemical, gustatory, and olfactory sensing systems;
• Signal preprocessing and feature-extraction methods;
• Machine learning and deep learning models for agricultural quality assessment;
• Field validation, industrial applications, and intelligent automation;
• Data fusion, multimodal sensing, and predictive analytics;
• Real-time decision-support systems.

We look forward to receiving your contributions.

Dr. Marcus Vinicius da Silva Ferreira
Dr. Toukir Ahmed
Guest Editors

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