Summary
This paper presents a comprehensive review of artificial intelligence techniques applicable to Internet of Things (IoT) environmental monitoring systems, with focus on the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) platform. The authors characterise seven categories of AI methodology—including predictive analytics, image classification, object detection, text spotting, auditory perception, natural language processing, and collaborative AI—by evaluating their software requirements, data I/O specifications, and computational demands. The proposed design framework aims to enhance SEMAR's capabilities for sensor data collection, display, and analysis, although implementation in live IoT applications is identified as future work.
UK applicability
This paper's relevance to UK agricultural and environmental monitoring is conditional on adoption of the SEMAR platform or similar IoT-AI architectures in UK farming and land management contexts. The technical framework could support UK environmental monitoring initiatives, though the abstract provides no evidence of testing in UK conditions or alignment with UK policy requirements.
Key measures
Software requirements; input/output data types; processing methods; computational characteristics of AI techniques in IoT applications
Outcomes reported
The paper reviews AI techniques applicable to IoT environmental monitoring systems and proposes design integration of AI methods into the SEMAR platform. It identifies characteristics of AI techniques including predictive analytics, image classification, object detection, NLP, and collaborative AI based on software requirements, data types, and processing methods.
Topic tags
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