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.

翻译：苹果园需要及时的病害检测、果实质量评估与产量估算，然而现有的基于无人机的系统往往孤立地处理这些任务，且通常依赖昂贵的多光谱传感器。本文提出了一种统一的、仅使用低成本RGB传感器的无人机果园智能流程，该流程集成了ResNet50用于叶片病害检测、VGG16用于苹果新鲜度判定，以及YOLOv8用于实时苹果检测与定位。该系统运行于ESP32-CAM与树莓派上，无需云端支持即可实现完全离线的现场推理。实验表明，叶片病害分类准确率达98.9%，新鲜度分类准确率达97.4%，苹果检测的F1分数为0.857。该框架为多光谱无人机解决方案提供了一种易于获取且可扩展的替代方案，支持在低成本硬件上实现实用的精准农业。