Classification of external defects on soybean seeds using multi-input convolutional neural networks with color and UV-induced fluorescence images input

Abstract

Since accurate soybean seed sorting is a crucial but time-consuming and labor-intensive process in soybean production, there is a need for an inexpensive and simple sorting method. The objective of this study was to classify soybean external defects by multi-input convolutional neural network (CNN) models with two types of images: color and UV-induced fluorescence images. Color and fluorescent images of soybean seeds were respectively taken by white and UV LED with a wavelength of 365 nm, and visually labeled into four categories: normal, wrinkled, peeled and defect. For classification, the multi-input CNN models were constructed using three patterns of pre-trained networks: AlexNet, ResNet-18 and EfficientNet. The classification accuracy of each model was evaluated with the test data which consists of 20% of the total data. As a result, the multi-input CNN models showed generally higher classification accuracy than single color images or fluorescence images input models. Furthermore, the highest classification accuracy was 93.9% with the multi-input CNN models using ResNet-18, where the accuracy was higher than single color images or fluorescence images input by over 6.0 pt. These results demonstrated that a multi-input CNN model combining conventional color images with fluorescence images has a potential for soybean external defect classification.