Semantic-Aware IoT Dataset for Smart Farming
This dataset focuses on semantic data modeling for smart farming, providing environmental logs and network metrics to optimize communication efficiency in large-scale IoT networks.
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This dataset focuses on semantic data modeling for smart farming, providing environmental logs and network metrics to optimize communication efficiency in large-scale IoT networks.
View DatasetAI-IoT-based Smart Drip Irrigation System (SDIS) dataset specifically designed for rice plants considering local agronomic characteristics, featuring soil moisture, weather data, and irrigation control decisions for precision agriculture.
View DatasetComprehensive bibliometric dataset of research publications on AI and IoT-based irrigation systems from Scopus and Web of Science (2006-2025), enabling systematic reviews, trend analysis, and research mapping in precision irrigation technology.
View DatasetReal-time sensor data for automated underground drip irrigation of tomato crops, including soil moisture, NPK (N, P, K), temperature, humidity, pressure, wind speed, and solar radiation collected via Edge IoT.[page:2][web:238]
View DatasetField data from an automated irrigation setup using capacitive soil moisture sensors and DHT‑11 air sensors, recording soil moisture, air temperature, humidity, and pump on/off status for smart irrigation control.[web:156]
View DatasetComprehensive IoT sensor dataset for precision agriculture with soil metrics (moisture, temperature, pH), environmental conditions (humidity, temperature, light intensity), and crop growth parameters. Includes irrigation recommendations and fertilizer optimization data.
View DatasetComprehensive IoT sensor data from smart farming systems. Includes soil moisture, temperature, humidity, NPK levels, and weather conditions for precision agriculture and crop yield optimization.
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