Dataset on irrigation for Tomato

This dataset is collected through real-time sensors to develop an automated underground drip irrigation system based on the Edge Internet of Things (IoT). Sensors used for collecting data include the BME280 temperature, humidity, and pressure sensors. Soil moisture value is measured through the capacitive SEN0193 soil moisture sensor. A 5-volt RS485 NPK sensor measures the N, P, and K values in mg/kg. A real-time API measures wind speed and solar radiation value based on the longitude and latitude of the farming field. Real-time data is collected in JavaScript Object Notation (JSON) and converted to CSV. Research in this study uses real-time data to test and analyze the automation of drip underground irrigation for tomato crops. The CSV format data are preprocessed and normalized to train the data for scheduling drip underground irrigation and predicting soil health status through an artificial intelligence approach. Several smart precision farming analysis methods for tomato crops can be applied using this data. For example, estimating total water demand and predicting soil and NPK fertilizer.