Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms
论文提出AI云边架构用于精准农业番茄病害检测,方法新颖,数据可靠,具有产业应用价值。
arXiv 2026年8月4日上线的预印本,提出一种面向互联精准农场的AI驱动云边架构。研究针对番茄早疫病、晚疫病、叶霉病等重大病害导致严重减产的问题,集成物联网传感器、无人机、深度学习、Azure IoT Hub云端分析与多平台(移动应用、Web应用、嵌入式边缘设备平台)接口,实现番茄病害实时检测。模型基于PlantVillage、Kaggle等公开数据集训练,通过TensorFlow模型部署至移动、Web与边缘设备平台。实验结果显示检测有效性达92-95%,在不同环境与设备平台表现一致,显著降低人工巡检依赖,实现及时干预,支撑可持续互联精准农场发展。
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Computer Science > Robotics
arXiv:2608.03816 (cs)
[Submitted on 4 Aug 2026]
Title:Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms
Authors:Deckshitha Angadi, Koteshwar Goud Surga, Nagaraju Lakkaraju, Naveena Budda, Vikas Agarwal, Giridhara Venkata Ram Raj Mulasa, Ravi Killamsetty, Chandrasekhara Sarma Mallubhotla, Narsimlu Kemsaram
View a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors
Abstract: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.
Comments:Accepted for presentation at the 2026 IEEE International Conference on Sustainable AI for Social Impact and Global Development (SASIGD 2026), Hyderabad, India, 13-14 August 2026
Subjects:Robotics (cs.RO)
Cite as:arXiv:2608.03816 [cs.RO]
(or arXiv:2608.03816v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.03816
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Narsimlu Kemsaram Dr [view email]
[v1] Tue, 4 Aug 2026 15:25:02 UTC (16,022 KB)
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View a PDF of the paper titled Design and Evaluation of an AI-Enabled Cloud-Edge Architecture for Connected Precision Agriculture Farms, by Deckshitha Angadi and 8 other authors
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