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An innovative approach that integrated series fusion of scatter correction techniques with deep learning models was introduced to achieve rapid and precise classification of common bunt in wheat kernels. Manual identification of this disease can be particularly challenging, especially in cases with mild infections. To address this challenge, the high-spectral data was leveraged from a sample set comprising 300 kernels, encompassing healthy, mildly infected, and severely infected kernels. The original spectra underwent preprocessing by using the multiplication scatter correction (MSC) and standard normal variate (SNV) algorithms. Furthermore, two-dimensional correlation spectroscopy (2D-COS) analysis was employed to assess the complementarity between spectra processed by SNV and MSC. Subsequently, the series fusion of scatter correction techniques was applied to amalgamate the original spectra, SNVprocessed spectra, and MSC-processed spectra, resulting in fused spectral sequences that harnessed the complementary information from various spectral preprocessing methodologies. Following this, a classification model for wheat common bunt, based on the ResNet 50 algorithm, was developed by using the fused spectral data. Experimental results demonstrated that the ResNet 50 model achieved the highest classification accuracy of 93.89% and an F1-score of 93.87%, surpassing models based on individual preprocessing methods. To further evaluate the classification performance of the model, partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and ensemble learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost) models were constructed by using the fused spectral data and comparison was done. The results revealed that SVM, PLS-DA, RF, and XGBoost achieved overall recognition accuracies of 81.67%, 84.44%, 89.44%, and 90.55%, respectively, with corresponding F1-scores of 81.59%, 84.04%, 89.49%, and 90.59%. Importantly, the ResNet 50 model outperformed traditional spectral analysis models in terms of overall accuracy and F1-score. In summary, ResNet 50 outperformed traditional spectral analysis models in terms of both overall accuracy and F1-score. In conclusion, this research underscored the efficacy of combining series fusion of scatter correction techniques with deep learning models for the classification of common bunt in wheat kernels at varying infection levels. This approach held promise for the development of rapid and non-destructive detection methods for common bunt in wheat kernels.