外文摘要：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 42degree celsius, 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.
外文关键词：image processing;color;BP neural network;Zanthoxylum armatum DC.;Grade classification
作者：Li, Jun;Cui, Qiang;Wang, Jiao;Xia, Dong;Wan, Junzhe;Hou, Xiaoyan;Shen, Guanghui;Li, Shanshan;Chen, Hong;Zhou, Man;Wang, Jie;Ren, Ran;Hu, Wen;Zhang, Zhiqing
作者单位：Sichuan Agr Univ;Sichuan Wufeng Lihong Food Co Ltd
期刊名称：SCIENTIA HORTICULTURAE
期刊影响因子：0.0
出版年份：2024
出版刊次：331
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台