Rapid detection of imperfect maize kernels based on spectral and image features fusion

In order to quickly and non-destructively detect imperfect maize kernels, and to effectively enhance the efficiency of maize quality detection during collection and storage, identification models for imperfect maize kernels were constructed by using hyperspectral imaging (HSI) combined with machine learning techniques. Hyperspectral images of maize kernels in the range of 380-1000 nm were collected, and 10 spectral characteristic wavelengths (variables) were selected by using variable combination cluster analysis (VCPA). For grayscale images corresponding to these characteristic wavelengths, 3 texture features were extracted by using Tamura algorithm. Additionally, 3 color features and 4 morphological features were extracted through color moment analysis and regional geometry calculation, respectively. Based on the data of spectral features, image features and fusion features (spectral and image features), partial least squares regression (PLSR) and extreme learning machine (ELM) algorithms were respectively used to establish identification models for imperfect maize kernels. The results demonstrated that the overall average recognition accuracy of the ELM models was 91.60%, slightly surpassing the 91.29% achieved by the PLSR models. Notably, the ELM model based on the fusion features exhibited the highest recognition accuracy for heat-damaged kernels, achieving an accuracy rate of 97.22% in the test set. Therefore, the classification models established in this study proved to be feasible for the rapid and accurate identification of imperfect maize kernels. This can provide valuable technical support for the research and the development of non-destructive, rapid inspection equipment for imperfect kernels, as well as online batch detection.