Detection Method of Soil Organic Matter Based on Multi-sensor Artificial Olfactory System
Abstract
In order to achieve a rapid, convenient and accurate measurement of the soil organic matter (SOM) content, a multi-sensor array based on artificial olfactory technology was designed to detect SOM content. Totally ten different types of oxide semiconductor gas sensors were selected to form the sensor array, and different concentrations of hydrogen sulfide, ammonia and methane standard gases were used to test the response of the sensor array. It can be seen from the response curve that the sensor array responded to different concentrations and types of standard gases, and the response results were not the same. With the increase of the standard gas concentration, the response curve of the sensor array was also increased. The test results showed that the sensor array had high specificity and certain cross-sensitivity. And from the soil gas response curve of each sensor, six features (response area value, maximum value, average differential coefficient, variance, average value and maximum gradient) were extracted to construct an artificial olfactory feature space (AOFS). Then, partial least square regression (PLSR), back propagation neural network (BPNN) and support vector machine regression (SVR) algorithms were used to establish the prediction model of AOFS and SOM content, and the coefficient of determination (R2), root mean square error (RMSE) and absolute average error (MAE) were used to evaluate the performance of the prediction model. The test results showed that the R2 of the PLSR, BPNN, and SVR test sets were 0.80878, 0.87179 and 0.91957, the RMSE were 3.6784g/kg, 3.1614g/kg and 2.4254g/kg, and the MAE were 3.1079g/kg, 2.4154g/kg and 2.1389g/kg, respectively. The model established by the SVR algorithm had the highest R2, the smallest RMSE and MAE. It had higher predictive performance than PLSR and BPNN, and can be used for the measurement of SOM content.
Keywords: soil organic matter, artificial olfactory system, gas sensor array, prediction method, regression algorithm
Download Full Text:
PDFReferences
YU Lei, HONG Yongsheng, GENG Lei, et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression[J]. Transactions of the CSAE, 2015, 31 (14): 103 -109. (in Chinese)
HUANG D, LIU H, ZHU L, et al. Soil organic matter determination based on artificial olfactory system and PLSR -BPNN[J]. Measurement Science and Technology, 2021 , 32(3) : 035801.
WU Caiwu, XIA Jianxin, DU AN Zhengrong. Review and prospect of soil organic matter determination methods [J]. Soil, 2015, 47(3) : 453 -460. (in Chinese)
LIU Jungen, WANG Wenjun, LUO Jiao, et al. Comparative analysis of soil organic matter content determination methods[J]. China Chemical Trade, 2017, 9(21): 212- 215. (in Chinese)
ZHAO Longhua, LIU Xiaofen, WANG Yajing, et al. Thermal analysis determining soil organic matter content and thermal stability [ J ]. Transactions of the CSAE, 2016, 32( 10) ; 105 - 114. (in Chinese)
CONDIT H K. The spectral reflectance of American soils[ J] - Photogrammetric Engineering, 1970, 36; 955 -966.
HUANG Yingfeng, LIU Tenghui. Spectral characteristics and soil distribution of main soil types in South Chirm [ J ]. Acta Pedologica Sinica, 1995 , 32(1 ) : 58 -68. (in Chinese)
STONE E K, BAUNGARDNER M F. Characteristic variations in reflectance of surface soils [ J ]. Soil Science Society of America Journal, 1981 , 45 : 1161 - 1165.
VAN W C, MESTDAGH I, LOOTENS P, et al. Possibilities of near infrared reflectance spectroscopy for the prediction of organic carbon concentrations in grassland soil[ J]- Journal of Agricultural Science, 2005, 143(6) ; 487 -492.
LIU W, BARET F, GU X, et al. Relating soil surface moisture to reflectance[ J ]. Remote Sensing of Environment, 2002, 81(2 -3) :238 -246.
BA Y, LIU J, HAN J, et al. Application of vis - NIR spectroscopy for determination the content of organic matter in saline- alkali soils[J]. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 2019, 229; 117863.
BOWERS S A, HANKS R J. Reflection of radiant energy from soil [J]. Soil Science, 1965, 1 (K)(2) ; 130 - 138.
CESAHE F D, MATTIA E D, PANTALEI S, el al. Use of electronic nose technology to measure soil microbial activity through biogenic volatile organic compounds and gases release [J]. Soil Biolog)' & Biochemistry, 2011, 43: 2094 -2107.
CONRAD R. Soil microorganisms as controllers of atmospheric trace gases ( H2, CO, CH4, OCS, N20, and NO) [ J ]. Microbiological Reviews, 1996 , 60(4) ; 609 -640.
JONATHAN W L, NOAH F. Volatile organic compound (VOC) emissions from soil and litter samples [ J]. Soil Biology and Biochemistry, 2008, 40(7): 1629 - 1636.
SHEPPARD S K, LLOYD D. Direct mass spectrometric measurement of gases in soil monoliths[ J]. Journal of Microbiological Methods, 2002, 50(2): 175 -188.
MCNEAL К S, HERBERT В E. Volatile organic metabolites as indicators of soil microbial activity and community composition shifts[ J]. Soil Science Society of America Journal, 2(X)9, 73(2); 579 -588.
FERNADO W G D, RAMARATHNAM R, KR1SHNAMOORTHY A S, et al. Identification and use of potential bacterial organic antifungal volatiles in biocontrol[ J], Soil Biology & Biochemistry, 2005, 37(5) ; 955 -964.
TOZLU В H, OKUMUS HI. A new approach to automation of black tea fermentation process with electronic nose [ J ]. Automatika, 2018, 59(3 -4): 373 -381.
SCHNABEL К M, BOUMANS M L L, SMOLINSKA A, et al. Electronic nose analysis of exhaled breath to diagnose ventilator- associated pneumonia[J ]. Respiratory Medicine, 2015, 109(11): 1454.
SCHMEKEL B, WINQUIST F, VIKSTROM A. Analysis of breath samples for lung cancer survival [J]. Analytica Chimica Acta, 2014, 840: 82 -86.
GIL-SANCHEZ L, SOTO J, MARTINEZ-MANEZ R, et al. A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine[ J]. Sensors and Actuators A; Physical,2011 , 171(2) ; 152 -158.
ANDRZEJ B, KATARZYNA J G, LUKASZ G, et al. Evaluating soil moisture status using an e-nose[J ). Sensors, 2016, 16 (6): 886.
LAVANYA S, NARAYANAN В D S, MURTHY V K, et al. Indicative extent of humic and fulvic acids in soils determined by electronic nose[J]. Computers and Electronics in Agriculture, 2017, 139: 198 -203.
Refbacks
- There are currently no refbacks.
