Forecasting of soil moisture using machine learning in smart agriculture systems

Abstract

Growing crops in modern conditions is a complex task and practically combines the practices of experience and the latest methods, including information technology, which has become part of the concept of "smart farming". An important factor in the stable predicted yield is the level of soil moisture, which is the result of changes in climatic factors such as air temperature, soil temperature, intensity of solar radiation, rainfall, wind speed, etc. A methodology for processing real historical indicators of climate change in a certain geographical area with subsequent training and application of machine learning models to predict soil moisture is proposed. To build a machine learning model, the following algorithms were selected and studied: the algorithm of regression trees, random forest, linear regression, M5P algorithms and the K* algorithm. The data source for training the models is the open information resource International Soil Moisture Network (ISMN) from ismn.earth/en. , which provides data on soil moisture and temperature, air temperature, and rainfall. Other data was used from the Open Meteo information service, which provides a free API and allows you to get historical data and weather forecast in specified coordinates during specified days. A data structure was developed to train the model for further prediction of soil moisture. An architecture has been developed and a software system for predicting soil moisture based on machine learning algorithms has been created using the Spring Framework, the WEKA library and Java FX with the ability to select and study the appropriate algorithms. Experiments have been carried out and the results of the duration of model training have been presented, while the algorithms of regression trees and linear regression require the least training time. A comparison of algorithms is made according to the following criteria: learning speed, cross-testing speed, prediction speed, testing performance indicators for real historical data. Based on the results of the study, conclusions are drawn about individual algorithms, the feasibility of using them to predict soil moisture based on climatic indicators. The obtained results will make it possible to evaluate and select the best models of machine learning in the design of the information and analytical system "smart agriculture" for forecasting soil moisture.