Intelligent Agricultural Systems Using IoT and AI

Abstract

The world today is under pressure to foster agriculture to be more productive with less water consumption, less fertilizer wastage, and less labor reliance. The Intelligent Agricultural Systems (IAS) combine Internet of Things ( IoT ) sensing, edges/cloud connectivity, and Artificial Intelligence (AI) instruments to facilitate precise choice, e.g., irrigating, applying nutrients, identifying diseases, and predicting harvests. This paper suggests an IoT aligned architecture of agriculture (i) multi-layer sensing of soil-crop-climate variables, (ii) edge intelligence of low-latency actuation, (iii) cloud analytics of model training and farm-level optimization, and (iv) secure data pipeline. The presentation of lightweight methodology is based on sensor fusion, anomaly detection, evapotranspiration-based water estimation, and machine learning models to classify irrigation and predict the crop stress. The performance of the system is measured in terms of common metrics (accuracy, F1-score, MAE, water-use efficiency), and a sample results discussion shows that the application of AI-based irrigation can decrease water consumption, but not the yield. The paper outlines such difficulties of deployment as connectivity gaps, sensor drift, explainability, and cyber-security and finishes by giving viable suggestions towards scalable deployment.