Classification of external defects on soybean seeds using deep learning with color and UV-induced fluorescence images input

Abstract

Since soybean seed sorting is a time-consuming and laborious process, there is a need for an inexpensive and simple sorting machine that can be used by a single farmer. The objective of this study was to classify the soybean external defects by using two types of images: a color image and a fluorescence image with an excitation wavelength of 365 nm. Color and fluorescence images of soybean seeds were captured, and manually labeled into four categories: normal, wrinkled, peeled, and pests. Deep learning models were constructed using ResNet-50 with three input patterns: color image, fluorescent image, and double input of color and fluorescence images. As a result, the test accuracy was 91.7%, 88.2%, and 88.3%, respectively. The model with double input of color and fluorescence images showed the highest precision in detecting healthy beans, and the visualization of the weights for classification revealed that the model emphasized healthy areas without defects. These results suggest that the combination of fluorescence images with conventional color images has the potential to classify the external defects on soybean seeds.