Design And Implementation Of Temperature Compensation System Based On BP Neural Network In The System Of Soil Moisture Measuring

Soil moisture is an important crop grown in each period of the reference conditions,appropriate or not directly related to soil moisture and yield of crops,and traditional agriculture for soil moisture determination depends entirely on the experience of farmers,it is not possible for traditional agricultural soil moisture accurately Analyzing now introduced agricultural soil moisture soil moisture sensor for digitizing the amount of control,greatly improving the accuracy required to control crop each growing season soil moisture,but also greatly reduces the traditional agricultural waste for irrigation water.Currently,soil moisture sensor for measurement acquisition and by the presence of non-linear output error,and humidity sensors at work susceptible to outside temperature,resulting in the use of the sensor with the outside temperature changes occur zero drift measurement data and produce more the phenomenon of large errors.In this paper,BP neural network,the data samples for training,in order to better eliminate the influence of soil moisture measurement error in temperature caused in the software level of soil moisture measurements is improved.This paper studies the working principle of soil moisture sensor and the performance indicators,while the market humidity sensor temperature compensation system is studied,the soil moisture sensor performance under a variety of temperatures were tested,and accordingly proposes the use of BP neural network software level of humidity sensors at work due to temperature changes and poor students errors phenomenon is corrected and offset,using Lab VIEW software simulation and humidity measurement system,the use of BP neural network design temperature compensation system for testing,tests show that the compensation system works well,and with the measurement data output nonlinear correction function,so it has great practical value.