Non-Destructive Discrimination of Sunflower Seeds with Different Internal Mildew Grades by Fusion of Near-Infrared Diffuse Reflectance and Transmittance Spectra Combined with 1D-CNN

Abstract

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.

Mildew detection in rice grains based on computer vision and the YOLO convolutional neural network

“…Liu et al. ( 2023 ) used near‐infrared spectroscopy to perform the classification of sunflower seed mold grades. Although this technology can detect slight mold with some accuracy, the spatial resolution of near‐infrared spectroscopy is very low.…”

“…Liu et al. ( 2023 ) proposed a method for identifying sunflower seed mildew grades based on near‐infrared diffuse reflectance and transmission fusion spectroscopy, along with a one‐dimensional convolutional neural network (CNN). However, generic near‐infrared spectrometers can only measure sample points and require the sample to be homogenized, which is not suitable for rapid rice contamination detection.…”

“…In addition, the above two methods can only detect moldy grains but cannot distinguish between different moldiness degrees. In this study, by analyzing the relationship between the TVC value of rice and the area of the mildewed areas identified by the model, it is possible to effectively distinguish between non-significantly moldy rice and slightly moldy rice Liu et al (2023). used near-infrared spectroscopy to perform the classification of sunflower seed mold grades.…”

Non-destructively qualitative and quantitative inspection methods based on THz spectroscopy and imaging

“…In a typical implementation, infrared spectroscopy is harnessed to determine the water content within sunflower seeds. 5,6 Through spectral analysis, researchers developed models that establish correlations between water content and spectral characteristics, thereby enabling efficient water monitoring and detection of mildew-infected kernels. In another application, near-infrared reflectance spectroscopy has been deployed to predict the fatty acid composition of sunflower seeds.…”

“…Non-destructive testing techniques present a superior alternative for assessing the quality of food and agricultural products. In a typical implementation, infrared spectroscopy is harnessed to determine the water content within sunflower seeds 5 , 6 . Through spectral analysis, researchers developed models that establish correlations between water content and spectral characteristics, thereby enabling efficient water monitoring and detection of mildew-infected kernels.…”