Author: Jiangbo Li

Publisher: Frontiers Media SA

Published: 2023-06-28

Total Pages: 117

ISBN-13: 283252673X

Quality and safety of agricultural products affect human nutrition and health. Agricultural products results from natural process influenced by a myriad of factors (e.g., genetics, environments before harvest, postharvest storage conditions). These factors lead to significant variations of agricultural products, externally and internally, which consequently pose great challenges with quality and safety evaluation. Development of advanced techniques for measuring agricultural product quality and safety is thus crucial to ensuring high quality, nutritious and safe food supplies. Non-destructive sensing technologies (e.g., optical, thermal, ultrasonic), in conjunction with advanced data analytics (e.g., machine learning) and control and automation technology, have evolved as a potent means for augmenting existing quality and safety control efforts of agricultural products, which largely rely on human or manual product assessment. With advancements in sensor and computer technologies, recent years have seen commercial-scale adoption of non-destructive sensing technology (e.g., machine vision, near-infrared spectroscopy) for postharvest quality evaluation for a diversity of specialty crop products (e.g., apples, citrus, kiwi). However, there are still numerous challenges with robust, high-performance detection of many quality and safety issues, such as subsurface/internal defects and contamination. This Research Topic covers the latest developments and applications of advanced non-destructive sensing technologies for quality and safety evaluation of agricultural products, with relevant areas including but not limited to: 1. Assessment of external and internal quality; 2. Detection of subsurface and internal defects; 3. Safety detection of agricultural products; 4. Design and development of advanced sensing system; 5. Multi-modal sensing and applications; 6. Online product grading and sorting; 7. Data handling methods (e.g., machine learning)