Variety identification of sweet maize seeds based on hyperspectral imaging combined with deep learning

Abstract

Seed classification and identification is of great importance for cultivar breeding. In this study, hyperspectral imaging technique combined with deep learning approaches are employed to identify the varieties of sweet maize seeds. First, a total of 1000 seeds, including five varieties of sweet maize seeds, are extracted and preprocessed. Secondly, support vector machine (SVM), K-nearest neighbors (KNN), extreme learning machine (ELM), and backpropagation (BP) are used for the establishment of classification models. Finally, convolutional neural networks (CNN), long short-term memory (LSTM), three-dimensional convolutional neural networks (3DCNN), multiple spectral resolution 3D convolutional neural network (MSR-3DCNN), spatial attention-based network (SATNet) and the proposed CNN-LSTM are applied to discriminate spectral images of sweet maize seeds. The experimental analysis results show that the deep learning model performed best with a classification accuracy of over 95% in the training and test sets. Besides, the performance of the proposed CNN-LSTM is slightly better than the other five models. The overall study demonstrated that deep learning combined with the hyperspectral image could potentially be a valuable alternative for identifying the variety of sweet maize seeds.