Design And Optimization Of Soil Major Nutrient Detection System Based On Pyrolysis And Olfactory Information

Soil is an important carrier for plant growth,and the nutrients in soil play a direct and crucial role in plant growth,development,and yield.Among them,the content of soil organic matter,total nitrogen,available potassium and available phosphorus are important indicators of soil fertility and nutrients.Therefore,obtaining the main nutrient content of soil quickly and efficiently is the foundation for maintaining soil fertility and maintaining good farmland quality.However,existing chemical detection methods have strong destructive power on soil and lack timeliness and simplicity.The purpose of this paper is to provide new research ideas and methods for rapid,sensitive,and low-cost of soil nutrient content by integrating engineering,agronomy,biology and other multi-disciplinary knowledge,and using interdisciplinary technologies such as pyrolysis technology,machine olfaction technology and electronic information engineering.Breaking through the challenges of traditional detection methods such as high cost and long detection cycle,promoting the intelligent and precise development of soil nutrient detection equipment.The main research content and results are as follows:(1)Based on the pyrolysis technology and machine olfaction technology,the design of soil nutrient content detection device.First,the pyrolysis technology was used to build a pyrolysis chamber to achieve high temperature and rapid cracking of soil samples at 400℃.Then,using machine olfactory technology,the gas from soil cracking is introduced into a sensor array composed of 20 different types of semiconductor gas sensors and the relevant gas concentration changes are detected.Finally,a data acquisition system consisting of a signal processing circuit,a collection card,and an upper computer developed with Lab VIEW software was designed to achieve the transmission,collection,display,and storage of sensor response data.Response testing and soil total nitrogen classification tests were conducted on the constructed detection device.All sensors maintained a stable output state after being powered on for a period of time,and produced unique and different intense responses after introducing soil high-temperature pyrolysis gas.The accurate classification of soil total nitrogen content levels laid the experimental foundation for the establishment of soil main nutrient content detection methods.This article innovatively designs a soil main nutrient content detection device that integrates multiple technologies.The device has the advantages of high sensitivity,simple operation,and low cost.(2)The original olfactory feature space of main soil nutrients was constructed and the optimization method of sensor array based on correlation coefficient and Mutual information was proposed.Based on the characteristics of sensor response data,the 7th second transient value,first-order average differential coefficient,maximum value,and relative stable state mean of the response curve smoothed and denoised by convolutional function were extracted,and 112×80(112 samples×20 sensors×4 features)were constructed dimension original olfactory feature space and completed the normalization of mean and variance of all data,and the normalized soil nutrient content data is generally Normal distribution.The correlation coefficient is used to analyze the sensor correlation,and the Mutual information is used to test the sensor combination with high degree of correlation,and the duplicate or redundant sensors are eliminated.Among them,the number of sensors covered by Soil organic matter,total nitrogen,available potassium and available phosphorus optimized by the sensor array is reduced by 2,1,7 and 5 compared with the original sensor array,which simplifies the scale of the sensor array and reduces the cross sensitivity between sensors,laying the foundation for the subsequent optimization of olfactory feature space and the establishment of prediction model.(3)Comparative study on spatial optimization methods for olfactory characteristics of major soil nutrients.Two feature selection algorithms(RF,Boruta)and two feature extraction algorithms(PCA,SPCA)were used to optimize the feature space of different soil nutrients after sensor array optimization,long short-term memory(LSTM)prediction model was established with coefficient of determination(R~2)as evaluation index.The results show that feature extraction algorithms PCA and SPCA can effectively reduce the complexity and scale of the feature space,with significant dimensionality reduction effects,but a small increase in R~2;In the Feature selection algorithm,Boruta algorithm,compared with the simple feature sorting of RF,takes into account the integrity of features,which maximizes R~2improvement.Among them,compared with the olfactory feature space optimized by sensor array,the R~2of soil organic matter,total nitrogen,available potassium and available phosphorus in the olfactory feature space after Boruta feature selection increased to 0.8231,0.8067,0.7149 and 0.6848,respectively,and the number of features decreased by 41,52,33 and 43,respectively.The number of sensors is reduced by 3,6,3 and 4,respectively and the R~2increased by 10.23%,9.38%,11.04%and15.96%,respectively,reducing the complexity of olfactory feature space,preserve broader olfactory spatial information with fewer sensors and feature dimensions.(4)Establishment and optimization of a prediction model for soil main nutrient content based on machine learning.Random forest(RF),support vector regression(SVR),partial least squares regression(PLSR)and least squares support vector machine(LSSVM)algorithms were used to establish prediction models for the olfactory feature space obtained from soil organic matter,total nitrogen,available potassium and available phosphorus after Boruta feature selection,coefficient of determination(R~2),root-mean-square deviation(RMSE),mean absolute error(MAE)and performance deviation ratio(RPD)were used as model evaluation indicators,among which LSSVM model had the best prediction performance.The sparrow search algorithm(SSA),particle swarm optimization(PSO),and whale optimization algorithm(WOA)were introduced to optimize the key parameters of the LSSVM model.Among the three optimization algorithms,PSO-LSSVM predicted values had the highest fitting degree with the true values,and the evaluation indicators and performance of the model were the best.The evaluation index R_v~2of PSO-LSSVM model test set for Soil organic matter,total nitrogen,available potassium and available phosphorus reached 0.9420,0.9400,0.8028 and 0.60119 respectively;The RMSE_Vreached 0.6928,0.0404,22.1000,and 4.6173,respectively;RPD_Vreached 4.1415,3.9081,2.0554,and 1.5476 respectively;MAE_Vreached 0.5631,0.0331,16.8424,and 3.6659respectively;the performance level of the model reached"excellent","excellent","qualified"and"poor"respectively.PSO-LSSVM model provides a more reliable Relational model for the prediction of Soil organic matter and total nitrogen content.Although the detection accuracy and model performance level of available potassium and available phosphorus content are lower than those of Soil organic matter and total nitrogen,they are also higher than the original olfactory feature space performance.It basically realizes the approximate quantitative prediction of available potassium and available phosphorus,providing a reference method for subsequent research.In conclusion,this paper has built a soil nutrient content detection system based on pyrolysis and machine olfaction,and optimized its sensor array,olfactory feature space and prediction model,which can basically achieve quantitative analysis and prediction of soil nutrient content,overcome the detection problem of"high cost and long detection cycle"of chemical methods,and break through the spectral analysis vulnerable to water The bottleneck of low accuracy is caused by the influence of iron oxide and other factors.This provides a fast,efficient,and economical new method for detecting the main nutrient content in soil.