A Soybean Classification Method Based on Data Balance and Deep Learning

Abstract

Soybean is a type of food crop with economic benefits. Whether they are damaged or not directly affects the survival and nutritional value of soybean plants. In machine learning, unbalanced data represent a major factor affecting machine learning efficiency, and unbalanced data refer to a category in which the number of samples in one category is much larger than that in the other, which biases the classification results towards a category with a large number of samples and thus affects the classification accuracy. Therefore, the effectiveness of the data-balancing method based on a convolutional neural network is investigated in this paper, and two balancing methods are used to expand the data set using the over-sampling method and using the loss function with assignable class weights. At the same time, to verify the effectiveness of the data-balancing method, four networks are introduced for control experiments. The experimental results show that the new loss function can effectively improve the classification accuracy and learning ability, and the classification accuracy of the DenseNet network can reach 98.48%, but the classification accuracy will be greatly reduced by using the data-augmentation method. With the binary classification method and the use of data-augmentation data sets, the excessive number of convolution layers will lead to a reduction in the classification accuracy and a small number of convolution layers can be used for classification purposes. It is verified that a neural network using a small convolution layer can improve the classification accuracy by 1.52% using the data-augmentation data-balancing method.

Keywords

• machine learning
• machine vision
• convolutional neural networks
• image processing