Non-Destructive Monitoring of Agricultural Product Quality

Agricultural product quality monitoring remains a critical challenge in achieving sustainable food systems. Traditional destructive methods, such as chemical assays or manual inspections, compromise sample integrity, generate waste, and lack real-time applicability. These limitations hinder rapid decision-making in post-harvest handling, storage, and supply chain management. Recent advances in non-destructive technologies—including hyperspectral imaging, near-infrared spectroscopy, and biosensors—offer transformative potential by enabling continuous, in-situ quality assessment without damaging produce. However, practical implementation faces hurdles, such as adapting these technologies to diverse agricultural products (e.g., fruits, grains, leafy greens), addressing variability in size, texture, and environmental conditions, and ensuring cost-effectiveness for small-scale producers. Additionally, integrating machine learning with sensor data to predict shelf-life, detect defects, or quantify nutrients requires robust validation across real-world scenarios. This Research Topic seeks to address these gaps by highlighting innovations that bridge laboratory research and field applications, emphasizing scalability, accuracy, and interoperability. By advancing non-destructive monitoring, stakeholders can reduce post-harvest losses, enhance traceability, and meet consumer demands for transparency and quality.

This Research Topic aims to showcase cutting-edge methodologies for non-destructive quality evaluation of agricultural products, from raw materials to processed goods. We seek to address critical challenges in adapting sensing technologies to diverse produce types, optimizing data analysis pipelines, and ensuring affordability for global supply chains. Contributions should explore how emerging tools—such as portable spectrometers, AI-driven imaging systems, or IoT-enabled sensors—can improve the detection of ripeness, spoilage, contaminants, or nutritional content. By fostering interdisciplinary dialogue among engineers, agronomists, and data scientists, this collection will establish guidelines for standardizing non-destructive techniques while highlighting their economic and environmental benefits. Ultimately, the goal is to accelerate the adoption of these technologies to minimize food waste, enhance safety, and support precision agriculture.

We welcome submissions, including but not limited to the following themes:


● Sensor technologies: Hyperspectral/multispectral imaging, NIR/FTIR spectroscopy, Raman spectroscopy, electronic noses for defect detection.

● Data analytics: Machine learning, deep learning, or IoT platforms for real-time quality prediction (such as freshness, nutrient density, or pesticide residues) and classification.

● Multimodal data fusion: Combining outputs from multiple sensors (eg., spectral gas-sensing) to enhance prediction accuracy for complex quality parameters.

● Product-specific applications: Techniques tailored to fruits, vegetables, cereals, dairy, or meat, focusing on ripeness, moisture, sugar content, or microbial contamination.

● Field-to-lab transition: Scalable hardware (low-cost portable devices, drone-mounted sensors) paired with user-centric interfaces (mobile apps, AR dashboards) for farmers and supply chain operators.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Agricultural product monitoring, Non-destructive testing, Quality evaluation, Machine learning, Spectral analysis

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.