Vol. 9, Special Issue 11, Part J (2025)

Seed quality assessment is essential for ensuring high crop productivity, maintaining varietal integrity, and strengthening global food security. Traditional seed testing methods such as germination testing, purity analysis, and moisture determination are often destructive, labor-intensive, and prone to human error, limiting their efficiency and reliability. Recent advancements in artificial intelligence (AI), machine learning (ML), deep learning (DL), and computer vision have transformed seed quality evaluation into a faster, more accurate, and non-destructive process. This review summarizes the principles of ML, key algorithmic categories, and their applications in seed quality analysis. Modern ML models including Support Vector Machines, Random Forests, Naïve Bayes, k-Nearest Neighbours, Artificial Neural Networks, and Convolutional Neural Networks demonstrate strong performance in seed classification, defect detection, vigor estimation, and variety identification. Integration of ML with multispectral, hyperspectral, terahertz, X-ray, and RGB imaging has enabled precise germination prediction, moisture estimation, and physical purity discrimination across diverse crop species. AI-based platforms such as SeedGerm, AIseed, and deep learning-driven phenotyping tools further enhance high-throughput and real-time analysis. Overall, the convergence of machine learning and advanced imaging technologies offers a transformative approach to seed quality assessment, delivering rapid, reliable, and non-destructive solutions that surpass traditional methods and support more efficient, data-driven agricultural systems.