MuST-C: Multi-Sensor Crop Phenotyping Dataset
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.
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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.
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 DatasetDataset 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.
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 DatasetNovel 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.
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.
View DatasetComprehensive 2024 agricultural data from California farms tracking environmental, soil, and crop metrics for predictive modeling.
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