This paper proposes an AI-enabled cloud-edge architecture for autonomous tomato disease monitoring. It combines IoT sensors, UAVs, deep learning, Azure IoT Hub analytics, and mobile, web, and embedded-edge interfaces. A TensorFlow model trained and validated with public sources including PlantVillage and Kaggle is deployed across the three platform types. The abstract reports roughly 92–95% detection effectiveness across environments and devices. However, it does not identify the exact metric, dataset split, sample size, model architecture, comparison baselines, latency, resource consumption, or real-farm trial scale.
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