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.
Chinedu Okafor· International Journal of Art...· 0 citations
Real-time data-streaming environments increasingly require adaptive decision mechanisms capable of coordinating heterogeneous computational resources while maintaining throughput, resilience, and service continuity under dynamic workloads. This paper proposes a conceptual adaptive artificial intelligence (AI) multi-agent model for optimizing real-time data streaming and system resilience. The proposed model combines autonomous agents for stream monitoring, workload allocation, resource coordination, anomaly response, and resilience management within a decentralized decision architecture. Its theoretical foundation is informed by research on distributed allocation, fairness, efficiency, optimization, and computational complexity in multi-agent decision environments. In particular, studies of fair and efficient allocation provide useful principles for balancing competing resource demands, while work on Nash social welfare and allocation algorithms demonstrates the value of optimization objectives that consider collective system utility. The proposed architecture extends these principles from indivisible-resource allocation toward dynamic streaming-resource management. The methodology defines agent roles, state representation, utility functions, adaptive allocation policies, coordination mechanisms, resilience procedures, and evaluation criteria. Analytical findings indicate that adaptive multi-agent coordination can improve resource utilization, reduce the impact of localized failures, and support scalable stream processing when compared conceptually with rigid centralized allocation. The model is particularly relevant to event-streaming environments in which workload intensity, resource availability, and service conditions change continuously. The paper further identifies limitations concerning coordination overhead, convergence, observability, and the absence of empirical benchmarking in the present conceptual study. The framework therefore provides a research foundation for implementing resilient AI-driven streaming systems and for future experimental validation.
Chinedu Okafor, Amara R. Eze· International Journal of Mod...· 0 citations
The analysis indicates that machine learning can shift test automation from static execution toward adaptive quality intelligence, however, model drift, insufficient representative test data, explainability, false positives, computational overhead, and continuous maintenance remain significant constraints.
Chinedu Okafor· International journal of dat...· 0 citations