A multimodal machine vision system for quality inspection of onions

A multimodal machine vision system was developed to evaluate quality factors of onions holistically and nondestructively. The system integrated hyperspectral, 3D, and X-ray imaging sensors. A LabVIEW program was developed to acquire color images, spectral images, depth images, X-ray images of onions, and measure the weight of onions. With the multimodal data collected, algorithms were developed to calculate the maximum diameter, volume, density, and detect latent defects of onions. Three groups of sweet onions (regular, inoculated with Burkholderia cepacia, and inoculated with Pseudomonas viridiflava) were tested. Results showed that the system accurately measured the weight (RMSE = 3.6 g), diameter (RMSE = 1.7 mm), volume (RMSE = 16.5 cm3), and density (RMSE = 0.03 g/cm3) of onions, and correctly classified 88.9% healthy and defective onions. This work demonstrated a promising approach to evaluate both external and internal quality parameters of onions, which is applicable to onion packinghouses. The proposed system and methods are also potentially applicable to quality inspection of other agricultural products.