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。
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
1.1本课题研究的主要内容
本研究的核心内容是开发一套基于深度学习的智能果蔬识别电子秤模拟系统。深入研究并选型适合果蔬识别的深度学习算法，分析卷积神经网络（CNN）及其经典架构（如AlexNet、VGG、ResNet等）在图像识别中的优势与不足，并针对果蔬的形状、颜色、纹理等特征进行优化，以提高识别准确率。设计高质量的图像采集方案，构建涵盖不同光照、摆放角度的果蔬图像数据集，并运用数据增强技术扩充数据集规模，提升模型的泛化能力。设计智能果蔬识别系统，包括图像预处理模块（去噪、归一化等）、特征提取与识别模块（基于深度学习模型）以及结果输出与反馈模块，以实现快速准确的识别与计价。还选择高精度电子秤硬件，设计硬件接口电路，实现图像采集设备、数据处理单元与电子秤的深度集成，开发控制软件以确保称重与识别功能的无缝衔接。最后进行全面的系统测试与优化，包括识别准确率、称重精度和系统稳定性测试，根据测试结果调整算法参数、改进硬件设计，以满足商业应用的高精度需求。
