Agricultural IoT network intrusion dataset with 1.31 million labeled flow records (532 MB) emulating a real AG-IoT farm environment. Covers crop health, weather, and soil condition data with network attack scenarios. CSV via Zenodo. Used for smart farming security research.
MuST-C is a multi-sensor agricultural dataset for in-field phenotyping, covering six crop species with RGB, LiDAR, and multispectral data to automate large-scale growth monitoring.
Curated collection of 10,000+ realistic IoT sensor readings from Bangladesh representing diverse environmental conditions including temperature, humidity, soil moisture, rainfall, and air quality with timestamps and location tags for smart agriculture and climate research.
AI-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.
Dataset documenting IoT sensor deployments in horticulture operations including greenhouse monitoring, fruit/vegetable cultivation parameters, and automated control systems for temperature, humidity, light, and nutrient delivery.
Comprehensive multi-sensor dataset with 9 parameters including environmental (temperature, humidity, light) and soil measurements (moisture, temperature, pH) plus solar battery voltage and water TDS, collected via Arduino-ESP8266 system with cloud integration.
Real-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]
Field 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]
Novel dataset combining IoT environmental sensors with robotic vision for automated plant disease detection. Published in Nature Scientific Reports January 2026. Features leaf images, environmental parameters, and deep learning disease classification with 98.9% accuracy.
Real-world IoT sensor dataset for precision agriculture and plant health monitoring. Includes environmental parameters (temperature, humidity, light) and soil metrics (pH, moisture, temperature) with Arduino-ESP8266 integration and cloud transmission.