用云边协同架构实现番茄病害实时自动识别，提升大农场监测效率。
Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms
- 融合物联网、无人机与深度学习，在云端和边缘设备部署AI模型。
- 在多种平台检测准确率达92%-95%，跨环境表现稳定。
- 适合农业科研机构与智慧农场管理者使用，推动精准植保。
植物病害导致全球作物产量显著损失，番茄尤其易受早疫病、晚疫病和叶霉病影响。人工监测仅适用于小规模农场，大规模下难以实施。为此，本文提出一种基于人工智能的云边协同架构，实现农作物的自主监测。该系统集成物联网传感器、无人机、深度学习技术、基于Azure IoT Hub的云分析，以及移动端、网页端和嵌入式边缘设备多平台接口，支持番茄病害的实时检测。训练与验证采用PlantVillage和Kaggle等公开数据集，使用自采数据集训练的TensorFlow模型被部署于移动、网页及边缘设备平台。实验结果表明，系统在不同环境与设备平台上均保持92%-95%的检测有效率，显著提升病害识别能力，减少对人工巡检的依赖，并支持及时干预，助力可持续的智能连通农业发展。
原文摘要 · Abstract (English)
Plant diseases cause significant yield losses worldwide, with tomato crops particularly susceptible to early blight, late blight, and leaf mold. Manual monitoring is practical only for small-scale farms and becomes unmanageable at larger scales. To tackle this limitation, an artificial intelligence (AI) enabled cloud-edge architecture is proposed for autonomous crop monitoring. This proposed architecture integrates Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), deep learning, Azure IoT Hub-based cloud analytics, and multi-platform (mobile app, web app, and embedded edge device platform) interfaces to enable real-time detection of tomato diseases. For training and validation, we used publicly available datasets, such as PlantVillage and Kaggle. A TensorFlow model trained on a collected dataset is deployed across mobile, web, and edge-device platforms. Experimental results show detection effectiveness around 92-95%, with consistent performance over diverse environments and device platforms. The proposed system improves disease detection effectiveness, lowers dependence on manual inspection, and enables prompt interventions, thereby supporting sustainable, connected precision agriculture farms.
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