arXiv 2512.229902026-01-01cs.CV

一种低成本无人机深度学习管道用于综合苹果疾病诊断、新鲜度评估和水果检测

A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection

Department of Electronics and Communication Engineering, Heritage Institute of Technology（电子与通信工程系，遗产理工学院）；Department of Computer Science and Engineering (AI & ML), Heritage Institute of Technology（计算机科学与工程系（人工智能与机器学习），遗产理工学院）

Soham Dutta, Soham Banerjee, Sneha Mahata, Anindya Sen, Sayantani Datta

备注：: This revision fixes minor typographical and phrasing issues

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AI总结：

本文提出一种低成本无人机深度学习系统，整合ResNet50、VGG16和YOLOv8，实现苹果疾病诊断、新鲜度评估和水果检测的综合应用。

摘要：

苹果果园需要及时检测疾病、评估水果质量以及估算产量，但现有的基于无人机的系统通常孤立地处理这些任务，并且常常依赖昂贵的多光谱传感器。本文提出了一种统一的、低成本的基于RGB的无人机果园智能管道，整合了ResNet50用于叶片疾病检测，VGG 16用于苹果新鲜度判断，以及YOLOv8用于实时苹果检测和定位。该系统运行在ESP32-CAM和Raspberry Pi上，提供完全离线的现场推理，无需云支持。实验表明，叶片疾病分类的准确率为98.9%，新鲜度分类的准确率为97.4%，苹果检测的F1得分为0.857。该框架为多光谱无人机解决方案提供了可及且可扩展的替代方案，支持在经济实惠的硬件上进行实际的精准农业。

英文摘要：

Apple orchards require timely disease detection, fruit quality assessment, and yield estimation, yet existing UAV-based systems address such tasks in isolation and often rely on costly multispectral sensors. This paper presents a unified, low-cost RGB-only UAV-based orchard intelligent pipeline integrating ResNet50 for leaf disease detection, VGG 16 for apple freshness determination, and YOLOv8 for real-time apple detection and localization. The system runs on an ESP32-CAM and Raspberry Pi, providing fully offline on-site inference without cloud support. Experiments demonstrate 98.9% accuracy for leaf disease classification, 97.4% accuracy for freshness classification, and 0.857 F1 score for apple detection. The framework provides an accessible and scalable alternative to multispectral UAV solutions, supporting practical precision agriculture on affordable hardware.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。