AI and IoT and Smart Sensor for Automated Plant Health Monitoring

Abstract

Precision agriculture increasingly relies on integrated Artificial Intelligence (AI), Internet of Things (IoT) platforms, and networks of smart sensors to enable continuous, automated plant-health monitoring. This paper examines the technological convergence that enables early detection of biotic and abiotic stressors, real-time decision support, and resource-efficient interventions. We synthesize sensor modalities (visible/NIR imaging, multispectral/hyperspectral cameras, chlorophyll fluorescence, soil moisture, temperature, relative humidity, gas analyzers for volatile organic compounds), on-node edge processing, and communication layers (LoRaWAN, NB-IoT, BLE, Wi-Fi) with AI methods spanning lightweight convolutional neural networks, transformer variants for remote sensing, time-series models for microclimate and soil data, and anomaly-detection frameworks for longitudinal plant health signatures. The discussion foregrounds practical deployment challenges — sensor calibration and drift, energy autonomy, data heterogeneity, network reliability in vegetated environments, domain shift across cultivars and phenological stages, and data governance including privacy and ownership — and examines mitigation strategies such as federated learning, domain adaptation, edge-cloud partitioning, and energy-aware scheduling. We conclude with a recommended systems architecture, an evaluation framework for field validation (accuracy, latency, false alarm cost, economic return), and a research roadmap that prioritizes robust multimodal fusion, scalable edge AI, and socio-technical adoption pathways for smallholder contexts. The paper aims to provide an actionable foundation for researchers and practitioners designing automated plant-health monitoring systems that are accurate, resilient, and economically viable.