Soil, crop, irrigation, greenhouse and aquaculture sensor data for precision farming, yield prediction and agricultural machine learning.
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.
Real-world smart farming operations data powered by IoT sensors and satellite data for crop yield prediction.
CropClimateX integrates satellite imagery, weather, and soil data across 1,527 US counties, enabling multi-task learning for yield prediction and climate impact analysis in agriculture.
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.
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.
Comprehensive 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.
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.
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.
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.
Comprehensive 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.
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.
Comprehensive IoT sensor data from smart farming systems. Includes soil moisture, temperature, humidity, NPK levels, and weather conditions for precision agriculture and crop yield optimization.
Comprehensive 2024 agricultural data from California farms tracking environmental, soil, and crop metrics for predictive modeling.
Standard precision agriculture dataset using N, P, K, pH, Temperature, and Humidity to recommend the ideal crop type.
Precision agriculture dataset using N, P, K, Temperature, and Humidity sensors to recommend ideal crops.
Precision agriculture data containing N, P, K, temperature, humidity, pH, and rainfall to recommend the best crop for the soil.
Precision agriculture dataset containing soil N, P, K values, temperature, humidity, and pH to recommend the best crop.
Comprehensive agricultural data tracking soil moisture, pH, nitrogen levels, and light intensity to determine overall plant health status.
Automated crop classification using morphological features captured by optical sensors for smart processing plants (Cammeo & Osmancik varieties).
Optimized dataset for smart farming, featuring soil nutrients (N, P, K), environmental sensors (Temp, Humidity, pH), and rainfall logs for 22 crop varieties.
Sensor readings from agricultural IoT nodes monitoring soil moisture, pH levels, and local weather conditions for irrigation automation