Advances in Non-Destructive Detection Technologies for Seed Quality: A Review

Seed quality profoundly affects productivity, marketability, and food security, yet conventional evaluation methods are destructive, slow, and unsuited to high-throughput screening. Non-destructive techniques, being rapid, non-invasive, and capable of measuring multiple indicators, have therefore gained substantial momentum. This review critically surveys the principles, applications, and limitations of major non-destructive techniques for seed quality assessment. Near-infrared spectroscopy (NIRS) enables fast, simultaneous multi-component analysis in portable formats, but its shallow penetration and poor sensitivity to subtle chemical shifts restrict single-seed vigor tests. Hyperspectral imaging (HSI) uniquely merges spectral with spatial data to map composition and surface defects, though large data volumes, high cost, and limited portability hinder practical use. Machine vision offers low-cost, high-throughput external sorting but captures only surface traits and is illumination-sensitive. X-ray/CT imaging visualizes internal cracks and insect damage, yet radiation safety and bulky hardware preclude field deployment. Complementary tools (NMR, electronic nose, Raman, dielectric, fluorescence, acoustic) address niche needs but face stability, sensitivity, or dimensionality trade-offs. Future breakthroughs demand multi-sensor data fusion, deep learning optimization, and ruggedized low-cost hardware. Bridging laboratory innovation and industrial reality requires concurrent algorithmic, optical, and engineering advances, ultimately transforming seed testing into a reliable, intelligent, and deployable ecosystem.