标题： DragonFruitQualityNet：一种轻量级卷积神经网络，用于移动设备上的实时火龙果质量检测 显示英文标题

标题： DragonFruitQualityNet: A Lightweight Convolutional Neural Network for Real-Time Dragon Fruit Quality Inspection on Mobile Devices

摘要： 火龙果因其营养益处和经济价值而闻名，由于其价格实惠和当地可获得性，全球需求不断上升。随着火龙果种植的扩展，高效的预处理和收获后质量检测已成为提高农业生产力和减少收获后损失的关键。本研究提出了DragonFruitQualityNet，这是一种轻量级卷积神经网络（CNN），专为在移动设备上实时评估火龙果质量而优化。我们整理了一个包含13,789张图像的多样化数据集，结合了自收集样本和公共数据集（来自Mendeley Data的数据集），并将其分为四个类别：新鲜、未成熟、成熟和有缺陷的果实，以确保模型训练的稳健性。所提出的模型实现了惊人的93.98%的准确率，在水果质量分类方面优于现有方法。为了促进实际应用，我们将模型嵌入到一个直观的移动应用程序中，使农民和农业利益相关者能够进行设备上的实时质量检测。这项研究为火龙果质量控制提供了一个准确、高效且可扩展的AI驱动解决方案，支持数字农业，并通过可访问的技术赋能小农户。通过弥合研究与实际应用之间的差距，我们的工作推进了收获后管理，促进了可持续的农业实践。 显示英文摘要

摘要： Dragon fruit, renowned for its nutritional benefits and economic value, has experienced rising global demand due to its affordability and local availability. As dragon fruit cultivation expands, efficient pre- and post-harvest quality inspection has become essential for improving agricultural productivity and minimizing post-harvest losses. This study presents DragonFruitQualityNet, a lightweight Convolutional Neural Network (CNN) optimized for real-time quality assessment of dragon fruits on mobile devices. We curated a diverse dataset of 13,789 images, integrating self-collected samples with public datasets (dataset from Mendeley Data), and classified them into four categories: fresh, immature, mature, and defective fruits to ensure robust model training. The proposed model achieves an impressive 93.98% accuracy, outperforming existing methods in fruit quality classification. To facilitate practical adoption, we embedded the model into an intuitive mobile application, enabling farmers and agricultural stakeholders to conduct on-device, real-time quality inspections. This research provides an accurate, efficient, and scalable AI-driven solution for dragon fruit quality control, supporting digital agriculture and empowering smallholder farmers with accessible technology. By bridging the gap between research and real-world application, our work advances post-harvest management and promotes sustainable farming practices.