随着人工智能技术的飞速发展，深度学习在图像识别领域的应用日益广泛。本文设计了一种基于深度学习的智能果蔬识别电子秤模拟系统，旨在提升果蔬零售行业的运营效率和消费者购物体验。系统通过内置的深度学习算法，能够快速准确地识别各类果蔬，并自动完成计价操作。在研究过程中，深入分析了多种深度学习算法，如卷积神经网络（CNN）及其经典架构，并结合果蔬图像的特征差异进行优化。同时，设计了高质量的图像采集方案，构建了丰富的果蔬图像数据集，并运用数据增强技术提高模型的泛化能力。此外，系统将智能识别模块与高精度电子秤硬件深度集成，实现了称重与识别功能的无缝衔接。经过全面测试与优化，该系统在正常光照条件下对常见果蔬的识别准确率可达98%以上，称重精度达到±0.1克，响应时间控制在1秒以内，满足商业应用的高精度需求。其应用不仅有效降低了人工成本，减少了计价错误，还为商家提供了精细化的数据管理支持，推动了果蔬零售行业的智能化发展。
关键词：果蔬识别；Python；django框架；模拟；
Abstract
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 ±0.1 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.
Key words:Fruit and Vegetable identification Python; django framework Simulation
第一章 概述
1.1 课题背景与意义
1.2 国内外研究现状
1.3 本课题研究的主要内容
第二章 开发工具及技术介绍
2.1 Django框架
2.2 Python语言
2.3 YOLO算法
2.4 MySQL数据库
2.5 CNN架构
第三章 系统分析
3.1系统性能分析
3.2系统可行性分析
3.3系统流程分析
3.3.1 登录流程图
3.3.2 添加新用户流程图
第四章 系统概要设计
4.1系统设计原理
4.2功能模块设计
4.3 数据库设计
4.3.1数据库设计原则
4.3.2数据库表结构设计
第五章 系统功能实现
5.1系统前台首页
5.2数据管理后台实现
5.3商品档案管理
5.4检测结果管理
第六章 系统测试
6.1系统测试的目的
6.2系统测试方法
6.3系统测试用例
结 论
致 谢
参考文献
