Soil Moisture Prediction Method Based on Machine Learning Algorithm

Soil moisture prediction is a technique for predicting soil moisture content. It is used in agriculture and hydrology to design irrigation systems, predict crop yields, monitor the impact of weather on crops, etc. The main purpose of soil moisture prediction (SPM) based on past data and climate information. SPM uses machine learning techniques, such as neural networks, k-means clustering, support vector machines, etc., to predict soil moisture at different time points in the growing season. The purpose of this technique is to classify soils into specific categories at an accurate and high-precision rate. It provides us with information about the moisture content of various soils, which is helpful to understand their characteristics and applicability for different purposes such as agriculture, engineering and construction. We can find that there are many methods that can be used to predict soil moisture, such as mathematical models, but they cannot provide accurate results samples from different locations.