用低成本摄像头实现苹果病害、新鲜度与果实检测一体化智能诊断
A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection
- 仅用普通摄像头，融合ResNet50、VGG16和YOLOv8模型实现三任务统一处理
- 病害分类准确率达98.9%，新鲜度分类97.4%，果实检测F1得分为0.857
- 可在ESP32-CAM与树莓派上离线运行，适合基层农业场景部署
苹果果园需要及时进行病害检测、果实品质评估和产量估算，但现有基于无人机的系统通常孤立处理这些任务，且常依赖昂贵的多光谱传感器。本文提出一种统一的低成本RGB-only无人机果园智能分析流程，集成ResNet50用于叶片病害识别，VGG16用于苹果新鲜度判断，YOLOv8用于实时苹果检测与定位。系统在ESP32-CAM和Raspberry Pi上运行，支持完全离线现场推理，无需云端支持。实验表明，叶片病害分类准确率为98.9%，新鲜度分类准确率为97.4%，苹果检测F1得分为0.857。该框架为多光谱无人机方案提供了可访问且可扩展的替代方案，支持在廉价硬件上实现实用精准农业。
原文摘要 · Abstract (English)
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.
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