Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.

Automated summary

In this study, an automatic approach based on machine vision technology is proposed for corn image acquisition, impurity classification and recognition, and impurities content detection. The MSRCR algorithm is used to enhance the image, HSV color space parameter threshold is set for image segmentation, and a comprehensive evaluation index is adopted for quantitatively evaluating the test results. The online detection results show the effectiveness of the proposed algorithm in identifying impurities in corn images and monitoring impurities content in the corn deep-bed drying process.