Journal of Experimental Agriculture International

Seed quality evaluation is undergoing a rapid transition from sample-based, time-intensive assays toward data-rich, non-destructive phenotyping capable of supporting faster, more objective decisions in modern seed systems. Multispectral imaging (MSI) provides a practical bridge between conventional visual inspection and chemical sensing by capturing spatial features together with wavelength-dependent reflectance and fluorescence responses from individual seeds. When integrated with artificial intelligence (AI), MSI enables automated classification, prediction, and localisation of quality-related traits, including viability and vigour differences, varietal purity signals, mechanical or processing damage, dormancy-linked hard seeds, stress history, and visible indicators of seed-borne health problems. This review synthesises recent advances in AI-driven MSI for seed quality assessment, with particular emphasis on standardised workflows enabled by VideometerLab platforms. Key elements covered include principles of MSI acquisition and calibration, segmentation and feature extraction, classical machine learning and ensemble approaches, deep learning for spatial–spectral representation learning, and explainable or human-in-the-loop methods that increase transparency and usability. The study discussed how model performance depends strongly on label quality, lot-wise validation, and robustness to domain shift caused by cultivar, season, moisture, and instrument variability. Practical deployment pathways are highlighted, positioning MSI+AI as a high-throughput screening layer that can prioritise confirmatory testing, reduce destructive sampling, and improve traceability in seed quality management. Finally, the review outlines future directions, including multimodal fusion with X-ray or spectroscopy, self-supervised learning for limited labels, and governance strategies for long-term model maintenance in operational seed laboratories.