Soil moisture content prediction method based on LSTM deep learning model

Abstract

The invention discloses a soil moisture content prediction method based on an LSTM deep learning model. The soil moisture content prediction method comprises the following steps: firstly, collecting soil physicochemical and meteorological data of a target farmland within a period of time; preprocessing the collected data, and dividing the preprocessed data into a training sample set and a test sample set; constructing an LSTM deep learning model; training the LSTM deep learning model through the training sample set to obtain a parameter-adjusted LSTM deep learning model, verifying the parameter-adjusted LSTM deep learning model through the test sample set, and finally taking the verified LSTM deep learning model as a soil moisture content prediction model; inputting the collected data intoa soil moisture content prediction model, and finally outputting a soil moisture content prediction result at a future moment through the soil moisture content prediction model. According to the method, the soil moisture content is forecasted by using a deep learning method, manpower and material resources are saved, the influence of early-stage data on a later-stage result can be truly reflected, and time sequence characteristics are fully embodied.

Description

technical field [0001] The invention relates to the field of soil moisture forecasting methods, in particular to a soil moisture forecasting method based on an LSTM deep learning model. Background technique [0002] my country is a country suffering from severe drought and water shortage, and its per capita water resources are only 1 / 4 of the world average level, making it one of the countries with the poorest per capita water resources in the world. The implementation of precision irrigation in the process of agricultural production can effectively save water resources and promote crop growth. However, there are often insufficient irrigation and excessive irrigation in farmland irrigation. Insufficient irrigation leads to stunted crop growth and low yield, while excessive irrigation can easily cause poor root development of crops and crop death, resulting in lower water resource utilization. Can not achieve the purpose of high yield. Therefore, establishing a soil moistur...

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Problems solved by technology

[0006] The purpose of the present invention is to provide a soil moisture prediction method based on the LSTM deep learning model to solve the problem that the prior art soil moisture prediction method does not consider the temporal characteristics of data

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