AI and IoT-Integrated Smart Agriculture: Predictive Crop Health Monitoring and Sustainable Resource Optimization using Cloud Platforms

Abstract

Agriculture faces increasing pressures from climate change, population growth, environmental degradation, and the depletion of natural
resources. To meet rising food demand sustainably, modern farming must become more datadriven, resilient, efficient, and adaptive.
Smart agriculture, integrating Internet of Things (IoT) sensors, cloud platforms, and artificial intelligence (AI), offers promise for realtime
monitoring of crop health, early disease and pest detection, precise irrigation and fertilizer control, and overall resource optimization.
This paper proposes a comprehensive framework combining IoT sensors deployed in the field (soil moisture, temperature, humidity,
spectral imaging, leaf health), AI models in the cloud for predictive crop health monitoring (disease, pest, nutrient deficiency), yield
forecasting, and resource optimization (water, fertilizer, energy). The architecture also includes feedback loops to send recommendations
to farmers via mobile/web dashboards, actuators for irrigation/fertilization control, and alerting in case of anomalies.
We conduct both simulation experiments and a pilot field deployment. Key evaluation metrics include disease/pest detection accuracy,
forecasting error (for yield and stress), water usage reduction, fertilizer use reduction, timeliness of detection (latency), system reliability,
and user acceptability. Our results show that predictive crop health monitoring with AI achieves detection accuracies of ~9098% for
common diseases/pests; yield forecasting errors (MAE/RMSE) are reduced by ~2540% compared with baseline statistical methods;
water usage for irrigation is reduced by ~3045%, fertilizer usage by ~2035% with maintained or increased yield; early alerts allow
interventions that cut disease spread significantly; cloudbased analytics enable scale, remote access, and integration of multisource
data (sensor + imaging + weather).
Tradeoffs include issues of data privacy, connectivity and latency, dependency on sensor quality, cloud costs, robustness to environmental
variability, and fairness in access for smallholder farmers. The study discusses how to mitigate these via edgecloud hybrid architectures,
lightweight models, transfer learning, data augmentation, local capacity building, and flexible hardware. We also explore socioeconomic,
policy, and regulatory challenges.
In conclusion, AIIoT smart agriculture systems on cloud platforms can significantly improve crop health, reduce resource consumption,
and support sustainable farming—especially when carefully designed for environmental, infrastructural, and socioeconomic constraints.
Future work will include larger scale deployments, extension to multispectral / hyperspectral sensing, robust models for new diseases,
better user interfaces, and exploring federated/cloud hybrid or edgeAI solutions to reduce dependence on connectivity.