Recent Advances in Seed Separation Technologies and Quality Evaluation of Major Grain Crops: A Review

Abstract

The growing demand for high-quality grains has intensified the need for efficient seed separation and quality evaluation methods in industrial processing, especially for food applications. Despite notable technological progress, conventional systems often lack the precision, speed, and scalability required for modern grain processing. This review synthesizes recent advancements in seed separation technologies and quality assessment techniques for major grain crops over the past decade (2015–2025). Relevant studies were identified through structured searches of three major scientific databases and screened for relevance to postharvest seed and grain processing. The review highlights developments in screening systems, vibrating sieves, airflow-based separators, and electrostatic technologies designed to improve sorting efficiency and reduce energy use. Emerging quality evaluation methods such as near-infrared hyperspectral imaging, FT-NIR spectroscopy, and micro-computed tomography imaging are examined for their capabilities in rapid trait analysis and structural inspection. The integration of machine learning and computer vision in seed sorting is also explored as a transformative approach to further enhance these systems. However, challenges such as non-uniform particle flow on separation surfaces, sensitivity to external conditions, calibration complexity, and high initial costs continue to constrain large-scale adoption. The review also outlines future research directions to center on cost-effective, scalable technologies, and AI-supported calibration strategies. These insights aim to guide stakeholders in seed processing, equipment design, quality control, and grain supply chains.