An intelligent and vision-based system for Baijiu brewing-sorghum discrimination

Abstract

A machine vision system was developed to discriminate in-demand and unwanted Baijiu brewing-sorghum at single kernel sample level. Three types of in-demand sorghum and seven types of unwanted sorghum were detected. Xception was employed to build classification model, reaching 89.08% and 88.21% correct classification rate for training and validation set, respectively. To achieve higher performance, two types of anti-aliased networks (anti-aliased max pooling (AntiMaxP) and anti-aliased convolutional (AntiConV)). Compared with the baseline Xception, the AntiMaxP and AntiConV both achieved higher overall accuracy. The AntiConV model obtained the best result, with accuracy of 89.22% and 89.15% for training and validation set, respectively. In view of practical application, the AntiConV model also obtained the most satisfactory result. Thus, AntiConV mdoel was integrated in the system. Adulterated samples were prepared to test the whole system. The results showed feasibility of the intelligent vision-based system to meet the practical application demands of Baijiu industry.