Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Convolutional Neural Network

Corn as a key food crop, has a wide range of varieties with similar appearances, making manual classification challenging. Thus, fast and non-destructive seed variety identification is crucial for improving yield and quality. Hyperspectral imaging is commonly used for non-destructive seed classification. For the advancement of smart agriculture and precision breeding, in this study, 30 corn varieties from Northwest China were analyzed using hyperspectral images (870-1709 nm) to extract spectral reflectance from the embryonic region. Traditional methods often involve selecting specific bands, which can lead to information loss and limited variety selection. In this study, information loss was reduced and manual intervention was minimized by using full-band spectral data. And preprocessing is performed using first-order derivatives to reduce the interference of noise and irrelevant information. Classification experiments were conducted using KNN, ELM, RF, 1DCNN, and an improved 1DCNN-LSTM-ATTENTION-ECA (CLA-CA) model. The CLA-CA model achieved the highest classification accuracy of 95.38%, significantly outperforming traditional machine learning and 1DCNN models. It is demonstrated that the innovative module combination method proposed in this study is able to successfully classify varieties of corn seeds, which provides a new option for the rapid and non-destructive identification of a variety of corn seeds.