土壤湿度预测模型训练数据

土壤湿度对农作物的生长、灌溉管理以及环境保护都具有重要影响。土壤温度对土壤湿度有较大影响。通过物联网设备采集同一地块的土壤温度、土壤湿度数据。结合土壤温度以及历史土壤湿度相关数据，建立土壤湿度预测模型，可以帮助预测土壤湿度变化趋势，指导农业生产和土壤管理。第一，在某一地块选定多个点位，部署物联网传感器，采集土壤温度、土壤湿度数据。共五个点位，五组传感器，每天早中晚三个时间点，将采集到的土壤数据，通过网络传送到服务器。
第二，同一个点位，同一天的不同时间点的土壤温度数据求和平均，得到日均土壤温度。同一个点位，任一个时间点的土壤湿度，减去前一天同一时间点的土壤湿度，得到日均土壤湿度差。
第三，基于支持向量机（SVM）方法，构建土壤湿度模拟与预测模型。使用历史土壤湿度、日均土壤温度、日均土壤湿度差数据，训练模型，预测土壤湿度变化趋势。

Soil moisture plays a crucial role in crop growth, irrigation management and environmental protection. Soil temperature exerts a significant influence on soil moisture. By collecting soil temperature and soil moisture data from the same plot via IoT devices, and combining soil temperature and historical soil moisture-related data, a soil moisture prediction model can be established to predict the changing trend of soil moisture and guide agricultural production and soil management.

First, select multiple sampling points in a given plot and deploy IoT sensors to collect soil temperature and soil moisture data. A total of 5 sampling points and 5 sets of sensors are deployed. Data collection is conducted three times a day (morning, noon and evening), and the collected soil data is transmitted to the server through the network.

Second, for the same sampling point, calculate the average of soil temperature data collected at different times on the same day to obtain the daily average soil temperature. For the soil moisture reading at any time point of the same sampling point, subtract the soil moisture reading at the same time point of the previous day to get the daily average soil moisture difference.

Third, construct a soil moisture simulation and prediction model based on the Support Vector Machine (SVM) method. Train the model using historical soil moisture, daily average soil temperature and daily average soil moisture difference data to predict the changing trend of soil moisture.

搜集汇总

数据集介绍

特点

该数据集为土壤湿度预测模型训练数据，包含661条记录，涵盖土壤温度、湿度等关键字段，通过物联网设备采集，应用于农业生产和土壤管理。采用支持向量机（SVM）方法构建预测模型，指导土壤湿度变化趋势预测。