Background: Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed.
Results: First, visible and near-infrared (NIR-visible) hyperspectral images were obtained. Savitzky-Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non-embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non-embryoid forms was 97.78% and 98.15%, respectively.
Conclusion: The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry.
Keywords: convolutional neural network; hyperspectral image; maize seed; subregional voting; variety identification.
© 2021 Society of Chemical Industry.