Online classification of soybean seeds based on deep learning

Abstract

To quickly evaluate soybean quality, we proposed a deep learning-based method for online classification of soybean seeds. Firstly, images of soybean seeds with uneven illumination were segmented based on the multi-scale Retinex with color restoration (MSRCR). Then, a convolutional neural network (CNN) was constructed to achieve soybean seed four-classification with appropriate parameters. The F-score of the normal, damaged, abnormal, and non-classifiable soybeans reached about 95.97%, 97.41%, 97.25%, and 96.14%, respectively. Finally, the method was successfully applied in NVIDIA Jetson TX2 with an accuracy of 95.63% and an average classification time of 4.92 ms for a soybean seed, which can meet the requirement of online soybean quality assessment.