Crop transpiration prediction method based on improved extreme learning machine

Abstract

The invention relates to a crop transpiration prediction method based on an improved extreme learning machine. First, soil environment data and meteorological data of a farmland are collected and normalized to obtain a training set; then, the training set is adopted to train an extreme learning machine network and improve an extreme learning machine; last, normalized data collected again is input into the improved extreme learning machine, and the improved extreme learning machine outputs crop transpiration obtained through prediction. Extreme learning machine improvement mainly comprises the steps that 1, a function based on waveform superposition is adopted to serve as an activation function for a hidden layer of the extreme learning machine; 2, a particle swarm optimization algorithm is adopted to optimize an input weight value and a threshold value between a network input layer and the hidden layer of the extreme learning machine. Through the prediction method, the prediction precision of crop transpiration is improved, prediction time loss is reduced, and meanwhile generalization performance and prediction stability of the traditional extreme learning machine network are improved.

Description

technical field [0001] The invention belongs to the field of intelligent irrigation research of the agricultural internet of things, and relates to a crop transpiration prediction method based on an improved extreme learning machine. Background technique [0002] The Agricultural Internet of Things is a highly integrated and comprehensive application of a new generation of information technology in the agricultural field. It plays an important leading role in the development of agricultural informatization in my country, changes the traditional agricultural production mode, and promotes the transformation of agriculture to the direction of intelligence and refinement. A large number of sensor nodes are used to collect real-time information on the crop production environment, and a monitoring system is formed through network technology to help farmers find problems in time and accurately determine the location of the problem. Turn the production mode that originally relied on...

AI Technical Summary

Problems solved by technology

Taking the weather forecast information as the basis and comprehensively considering the influence of environmental factors such as the growth and development of the crop itself, meteorological conditions, and soil conditions to estimate the forecasting model for the water demand of winter wheat reference crops, this method requires a large amount of information to be collected, and is specific to specific crops. , the degree of applicability is difficult to promote

The invention patent "on-line control and management method of field intelligent irrigation" (application number: 201410655632.1) proposes an online control and management method of field intelligent irrigation. The soil moisture content in the root zone of crops is measured in real time through soil water environment monitoring equipment, and the data acquisition system The real-time collected data is analyzed, summarized, processed and other secondary processing to calculate the real-time transpiration and evaporation of crops. This method has limited prediction accuracy for crop transpiration. In addition, the special equipment used to monitor soil moisture content is relatively expensive.

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