The Study Of Measurement For Corn Moisture Content By Using Mechanics And Acoustics

In appraising corn quality,the moisture content of corn is a basic item.Corn moisture measurement has very important meaning for corn production,storage,selling and so on.In addition,the veracity of measurement moisture has direct relation with the economical profit of state and nation.This paper measures the moisture content of corn making use of mechanics and acoustics property together,and carries out following works mainly,and has gotten related conclusion:1.Designing the experimentation equipment Designing the fragmentation equipment of corn kernel for the need of experimentation.The equipement is make up of foundation,handle,head,module and so on.The groove in the peak of head is used to prevent to the slipping of corn.There is a flute in the peak of the theca,and it is used to placed corn in order to avoid to the splash of corn.2.Determining experiment parameters This paper studies on corn kernel size, compress position,sort, deposited time, compress velocity,the distance between sound sensor and sound sourse.The corn kernels which have more than 70 percent of appearance frequency are selected,the length between 1.1 centimetre and 1.4 centimetre,the width between 0.8 centimetre and 1.1 centimetre,the thickness round about 0.5 centimetre;Differ sorts of corn kernels have differ capacity of resisting to fragmentation,this paper selects Great Wall 799,Jidan 209,Yidan No2 corn sorts to carry out end,edge and side oritentation experiments;Differ deposited time corns have no extremely effect on experiments.The long deposited corn's scarfskin is so prone to crack that has effect on the result of experiments as result of the low moisture;The testing datas are relative stabilization when experimentations are processed at the speed of 16~24mm/s;The acoustic power decreases as the distance between sound sourse and sound sensor as sound increases,and they present linear relation.This paper chooses 1 centimetre as the distance between sound sourse and sound sensor.3.Analyzing mechanics signal and selecting parameters The most fragmentation power of three kinds of compress modes,end,edge and side oritentations, of Great Wall 799,Yidan No2 corn take on the tendency of descend following the increase of the moisture; When ,Jidan 209 corns are placed in end or edge oritentations,the relation between moisture and mechanicss's value is linear,but placed in side oritentation.Eventually,the most fragmentation powers of end,edge and side oritentations are the parameters that describe mechanics's property of Great Wall 799 and Yidan No2 corns;And the most fragmentation powers of end and edge oritentations are the parameters that describe mechanics's property of Jidan 209.The corn which kernels are flat has effect on the side fragmentation.4.Analyzing sound signal and selecting parameters Analysising sound signal in time field and frequency field,including signal power,wave form correlation coefficient of time field, the margin of breadth value,spectrum energy,spectrum pick value,spectrum pick location and signal waveform correlation of frequency field.This paper studies sound signal of three compress locations of three kinds of corns,Great Wall 799,Jidan209,Yidan No2.The analytical conclusions indicate:when Great Wall 799 corns are placed in edge oritentation,the sound signal power descends as the moisture content of corn increases,and in end ,edge,side oritentation,the margin of breadth value and spectrum energy are certain linear relation to the moisture content; when Jidan 209 corns are placed in edge oritentation,the sound signal power descends as the moisture content of corn increases,and the margin of breadth value and spectrum energy in end ,edge,side oritentation take on linear relation as the moisture increases,and the signal waveform correlation of frequency field in end oritentation descends following the increase of the moisture content;When Yidan No2 corns are crushed in end,edge,side oritentations, the margin of breadth value,spectrum energy and the signal waveform correlation of frequency field in end oritentation show a certain change tendency with the increase of the moisture content.This paper confirms that the parameters describe Great Wall 799 corn sound signal property are sound signal power in edge oritentation, the margin of breadth value and spectrum energy in end ,edge,side oritentation,that the parameters describe Jidan 209 corn property are sound signal power in edge oritentation, the margin of breadth value and spectrum energy in end ,edge,side oritentation,and the signal waveform correlation of frequency field in end oritentation,that the parameters of Yidan No2 corn are the margin of breadth value and spectrum energy in end ,edge,side oritentation,and the signal waveform correlation of frequency field in end oritentation.Different kinds of corn have effect on some parameters of acoustics,but not most.5.Analyzing the correlation between mechanics and sound parameters and corn moisture content Analyzing the relativity of mechanics and acoustics parameters,and picking mechanics-sound parameters to forecast corn moisture through clustering analysis means.Multivariate linear regression,primary component linear regression and neural network are used to analyze the relation between mechanics and acoustics parameters and moisture content,and linear regression equation,primary component regression equation about mechanics,sacoustics and mechanics-acoustics of Great Wall 799,Jidan 209,Yidan No2 are obtained.Forecasting corn moisture content through the gotten regression equation and neural network.The results indicate that the most relative error of linear equations gotten from the mechanics, acoustics,mechanics-acoustics parameters forecast corn moisture are 0.09,0.16,0.07.When using multivariate linear regression,the relative error of mechnics-acoustics parameters is less than the other methods;When usling the primary component linear regression equations,the most relative error of acoustics and mechanics-acoustics parameters are 0.32,0.16 separately.It seems that the relative error of using mechanics-acoustics parameters to forecast corn moisture is less than the other one;When using neural network to forecast corn moisture,the most relative error of three methods,mechanics,acoustics,mechanics-acousti s are 0.16,0.07,0.03 separately.It seems that the result of mechanics-acoustics method is good when neural network forecasts corn moisture content.6.Comparing the accuracy rate of forcast models The threshold value is 0.25,when using mechanics parameters forecast corn moisture,the accuracy rate of the multivariate linear regression model and the neural network are 60%,63.3%;When using acoustics parameters forecast corn moisture,the accuracy rate of the multivariate linear regression model,the primary component linear regression model,and the neural network are 48.3%,52%,75.7%;When using mechanics-acoustics parameters forast corn moisture, the accuracy rate of the multivariate linear regression model,the primary component linear regression model,and the neural network are 68.3%,67%,96.7%. The threshold value is 0. 5,when using mechanics parameters forecast corn moisture,the accuracy rate of the multivariate linear regression model and the neural network are 71.7%,84.3%;When using acoustics parameters forecast corn moisture,the accuracy rate of the multivariate linear regression model,the primary component linear regression model,and the neural network are 76.7%,63.7%,86%;When using mechanics-acoustics parameters forecast corn moisture, the accuracy rate of the multivariate linear regression model,the primary component linear regression model,and the neural network are 86%,77.7%,99.3%.It can be seen that the effect of mechanics-acoustics parameters forecasts moisture is better than the effect of mechanics or acoustics parameter,and the accuracy rate of neural network is best when using mechanics-acoustics parameters to forecast corn moisture content.