Color grading of green Sichuan pepper(Zanthoxylum armatum DC.)dried fruit based on image processing and BP neural network algorithm

•Morphological processing and feature value extraction of the collected dried fruit images of Z. armatum DC.•Convert RGB color model to HIS color model to obtain color characteristic values.•BP neural network model was constructed to identify and classify the dried fruit of Z. armatum DC. In order to achieve rapid and accurate grading of Zanthoxylum armatum DC. dried fruit, this study utilized image processing technology combined with BP neural networks for the dried fruit of Z. armatum identification to enhance the market value of Z. armatum and their products. The results showed that preprocessing the images with a yellow background yielded the best results, effectively distinguishing the external contours and internal images of Z. armatum. The extracted image of the dried fruit can represent information about its peel, with R, G, and B values being the largest at 69.3285, 65.6432, and 31.2561, respectively. Among the seven drying methods, the average G value was highest at 71.0560 under the conditions of blowing at 42℃, whereas it was lowest at 47.3840 under sunlight drying. Moreover, R, G, B, H, S, and I represent the input layer nodes of the BP neural network. The change in the number of hidden layers did not correlate with the classification results. The classification accuracy of the BP neural network for Z. armatum classification under all dry conditions was 79.53 %-98.04 %, indicating high reliability. The proposed method can provide a theoretical basis for future research on Z. armatum classification. A schematic diagram of color grading of green Sichuan pepper(Zanthoxylum armatum DC.)dried fruit. [Display omitted]