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.本数据集通过实时传感器收集，旨在开发基于边缘物联网（Edge IoT）的自动化地下滴灌系统。数据收集所使用的传感器包括BME280温度、湿度和压力传感器。土壤水分值通过电容式SEN0193土壤水分传感器进行测量。一款5伏RS485 NPK传感器用于测量每千克土壤中的氮、磷、钾含量。基于农场的经纬度，实时API测量风速和太阳辐射值。实时数据以JavaScript对象表示法（JSON）格式收集，并转换为CSV格式。本研究中的研究利用实时数据对番茄作物的地下滴灌自动化进行测试和分析。CSV格式的数据经过预处理和标准化，以训练用于安排地下滴灌调度和通过人工智能方法预测土壤健康状况的数据。可利用此数据应用多种针对番茄作物的智能精准农业分析方法。例如，估算总需水量以及预测土壤和NPK肥料的含量。