{"id":63610,"date":"2026-01-22T02:17:15","date_gmt":"2026-01-21T18:17:15","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/63610.html"},"modified":"2026-01-22T02:17:15","modified_gmt":"2026-01-21T18:17:15","slug":"%e8%ae%a1%e7%ae%97%e6%9c%ba%e6%af%95%e4%b8%9a%e8%ae%be%e8%ae%a1%e7%9a%84python%e6%9e%9c%e8%94%ac%e8%af%86%e5%88%ab%e7%94%b5%e5%ad%90%e7%a7%a4%e6%a8%a1%e6%8b%9f%e7%b3%bb%e7%bb%9f%ef%bc%88%e5%bc%80","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/63610.html","title":{"rendered":"\u8ba1\u7b97\u673a\u6bd5\u4e1a\u8bbe\u8ba1\u7684python\u679c\u852c\u8bc6\u522b\u7535\u5b50\u79e4\u6a21\u62df\u7cfb\u7edf\uff08\u5f00\u9898\u62a5\u544a\u4efb\u52a1\u4e66\uff09"},"content":{"rendered":"

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\n<\/p>\n

Abstract<\/p>\n

With the rapid development of artificial intelligence technology, the application of deep learning in the field of image recognition is becoming increasingly widespread. This paper designs an intelligent electronic scale simulation system for fruit and vegetable recognition based on deep learning, aiming to enhance the operational efficiency of the fruit and vegetable retail industry and the shopping experience of consumers. The system, through its built-in deep learning algorithm, can quickly and accurately identify various fruits and vegetables and automatically complete the pricing operation. During the research process, a variety of deep learning algorithms, such as convolutional Neural networks (CNNS) and their classic architectures, were deeply analyzed and optimized in combination with the feature differences of fruit and vegetable images. Meanwhile, a high-quality image acquisition scheme was designed, a rich dataset of fruit and vegetable images was constructed, and data augmentation techniques were applied to improve the generalization ability of the model. In addition, the system deeply integrates the intelligent recognition module with the high-precision electronic scale hardware, achieving seamless connection between weighing and recognition functions. After comprehensive testing and optimization, the system can achieve an accuracy rate of over 98% for common fruits and vegetables under normal lighting conditions, with a weighing accuracy of \u00b10.1 grams and a response time controlled within 1 second, meeting the high-precision requirements of commercial applications. Its application not only effectively reduces labor costs and pricing errors, but also provides merchants with refined data management support, promoting the intelligent development of the fruit and vegetable retail industry.<\/p>\n

\n

Key words:\u00a0Fruit and Vegetable identification Python; django framework Simulation<\/p>\n<\/p>\n

\u76ee \u00a0\u5f55<\/p>\n

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