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Abstract
Moldy maize can produce a lot of toxins, which is harmful to human and livestock. Therefore, early detection of maize mildew is of great significance. In this study, the hyperspectral image data of maize seed with five mildew grades of the same variety were selected as the data source, by comparing a variety of preprocessing and feature extraction methods, the combination method of standard normal variate and uninformative variable elimination was selected to process hyperspectral data. In view of the shortcomings of traditional BP neural network, such as easy to fall into local optimum and slow convergence speed, BP network with ant colony optimization classification model was established by introducing ant colony optimization weight threshold. Support vector machine based on linear kernel, support vector machine based on quadratic kernel and BP neural network model were compared and the classification results were analyzed. The results show that the standard normal variate and uninformative variable elimination can effectively eliminate the error caused by solid particle surface scattering and reduce the amount of data. Among the four recognition models, BP network with ant colony optimization has the highest classification accuracy, the overall classification accuracy reaches 92.00%, which is 8.00% higher than that of the BP neural network, 12.00% higher than the support vector machine with linear kernel function and 16.00% higher than the support vector machine with quadratic kernel function, indicating that the ant colony optimization can effectively improve the recognition accuracy of the BP neural network model. This paper can provide technical support and new ideas for maize seed early mildew detection and maize seed selection.