A Review of Artificial Intelligence Methods for Seed Quality Inspection based on Spectral Imaging and Analysis

Abstract

The quality inspection of seeds is of great importance to agricultural production. Traditional detection methods, such as chemical treatment, hollow determination, hyperosmotic germination, electrophoretic analysis, are time-consuming and inefficient. On the contrast, spectral imaging combined with image processing is non-destructive, which is promising in rapid quality inspection of seeds. This review analyzes the characteristics of spectral analysis as well as spectral imaging technology. The advantages of applying near-infrared spectroscopy and spectral imaging technique in quality inspection of seeds and their current application status is introduced, and the research progress of image processing algorithms is described.