Hyperspectral Technology for Precision Plant and Food Analysis: Unveiling Chemical Composition and Quality Parameters

Dear Colleagues,

Over the past decade, hyperspectral imaging (HSI) has rapidly evolved into a widely adopted analytical technique across the food and agricultural sectors. Recent instrumentation advances have enabled the high-throughput collection of rich spectral data to be unparalleled by any other optical sensing method. The integration of hyper/multispectral imaging into moving platforms and production lines now permits the real-time, non-invasive evaluation of multiple quality parameters. From pre-harvest field phenotyping to postharvest packing facilities, hyperspectral analysis creates new paradigms for safety and quality management across the supply chain. This Special Issue spotlights the most innovative advancements in HSI technology and reveals its immense application potential for enhanced precision in plant and food systems.

This Special Issue welcomes original research articles, reviews, and technical notes focusing on the following key areas:

1. Advances in hyperspectral sensors for field and postharvest applications;
   • New sensor designs, hardware integration, and deployment strategies for portability;
   • Enabling in-line, real-time analysis across the agri-food chain.
2. Novel hyperspectral image acquisition and correction techniques;
   • Methods to improve spectral quality, eliminate noise, and enhance resolution.
3. The development of robust spectroscopic calibration models;
   • Strategies for representative sampling and model transferability.
4. Chemometrics for hyperspectral big data analytics;
   • Machine learning for predictive modeling and pattern recognition;
   • Strategies for handling high-dimensional spectral data.
5. Data fusion platforms and decision support systems;
   • Integrating spectral data with spatial, temporal, and external data sets;
   • Real-time systems for supporting on-site decision making.
6. The development of HSI-based precision agriculture practices for optimizing crop yield and quality;
   • Site-specific management based on spatially resolved crop status monitoring;
   • Early stress detection enabling timely intervention to maximize yields;
   • Estimating yield/quality attributes for selective harvesting and grading.
7. Real-time monitoring systems for postharvest quality assurance;
   • The early detection of defects, contaminants, and disorders;
   • The non-destructive evaluation of maturity and shelf-life dynamics.

Dr. Alireza Sanaeifar
Guest Editor

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• hyperspectral imaging
• spectroscopy
• chemometrics
• precision agriculture
• phenotyping
• food quality
• safety assessment
• data fusion
• sustainability