Dataset on Irrigation for Tomato – Real-Time IoT Sensors for Smart Drip Irrigation

Overview

The Dataset on Irrigation for Tomato is a collection of real-time sensor measurements used to develop and test an automated underground drip irrigation system based on Edge Internet of Things (IoT) for tomato cultivation.[page:2][web:238]

Data Collection

• Sensors deployed in the field include BME280 (temperature, humidity, pressure), SEN0193 capacitive soil moisture sensor, and a 5-volt RS485 NPK sensor measuring nitrogen, phosphorus, and potassium in mg/kg.[page:2]
• Additional parameters such as wind speed and solar radiation are obtained via a real-time weather API based on the field's longitude and latitude.[page:2]
• Data were initially collected in JSON format and then converted to CSV for analysis and model training.[page:2]

Variables

• Soil moisture: Measured by the SEN0193 capacitive sensor, used as the primary control variable for irrigation scheduling.[page:2]
• NPK values: Nitrogen (N), Phosphorus (P), and Potassium (K) concentrations in mg/kg, essential for assessing soil fertility and fertilizer needs.[page:2]
• Temperature, humidity, and pressure: Environmental conditions captured by the BME280 sensor at the field location.[page:2]
• Wind speed and solar radiation: Retrieved from weather API to model evapotranspiration and water demand.[page:2]

Use Cases

• Training machine learning models to schedule drip irrigation and predict total water demand for tomato crops.[page:2]
• Analyzing and forecasting soil health status and fertilizer requirements using NPK measurements.[page:2]
• Developing Edge IoT solutions for precision irrigation that respond in real time to soil and environmental conditions.[page:2][web:238]

License

The dataset is hosted on Mendeley Data with DOI 10.17632/33cngpcrmx.2 (Version 2); users should consult the Mendeley Data page for the explicit license and reuse terms before using the data.[page:2]