https://doi.org/10.4081/jae.2022.1472
Application of ground penetrating radar technology in moisture content detection of stored grain
Published: 15 September 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
How to detect grain moisture content storage inefficiently, non-destructively, and quickly is a critical task in the storage process of the modern grain industry. The influence of media with different moisture content on the propagation and attenuation of electromagnetic wave energy is the premise and basis for applying electromagnetic wave technology in detecting grain moisture content. To explore the applicability of electromagnetic wave technology in detecting grain moisture content, we used ground penetrating radar (GPR) technology and autoregressive and moving average (ARMA) power spectrum analysis method to detect and study the moisture content of the typical national grain depots and local grain depots. The results show that GPR technology could realise the moisture content of stored grains and solve the problems of detection distance, non-destructive, and detection dead ends. Compared with the actual test data, the correlation is above 90%, the error can be controlled within 0.5%, and the measurement accuracy is higher, within ±0.3%. Furthermore, the continuous distribution profile of stored grain moisture content was obtained using the ARMA method. The moisture content distribution range of the rice barn was 10-14%, showing the regularity of the moisture content distribution in the middle layer > upper-middle layer > lower-middle layer > bottom layer > grain surface layer. It indicates that the GPR technology has particular advantages in food safety detection and provides data support for real-time detection of food storage safety.
Anbazhagan P., Bittelli M., Pallepati R.R., Mahajan P. 2020. Comparison of Soil Water Content Estimation Equations using Ground Penetrating Radar. J. Hydrol. 588:1-9.
Bian Z.F., Lei S.G., Hilary I. 2009. Integrated Method of R.S. And GPR for Monitoring the Changes in the Soil Moisture and Groundwater Environment Due to Underground Coal Mining. Environ. Geol. 57:133-42.
Bu F.Y., Han J.Z. 2007. Application of Non-Destructive Testing Technology in Food Quality Testing. Sci. Technol. Food Ind. 28:221-4.
Cadzow J.A. 1980. High Performance Spectral Estimation-A New ARMA Method. IEEE Transactions on Acoustics, Speech & Signal Processing. 28:524-9.
Chen C. 2001. Moisture Measurement of Grain Using Humidity Sensors. Trans. ASABE. 44:1241-5.
Cui F., Chen B.P., Wu Z.Y., Nie J.L., Li S.Y., Geng X.H., Li S. 2018. Soil Moisture Estimation Based on GPR Power Spectrum and Envelope Amplitude in Sand Loam. TCSAE. 34:121-7.
Cui F., Liu J., Wu Z.Y. 2014. Application of Ground Penetrating Radar Power Spectrum Model in Detection of Water Content and Degrees of Compactness in Sandy Loam. TCSAE. 30:99-105.
Cui F., Wu Z.Y., Wang L., Wu Y.B. 2015. Application of the Ground Penetrating Radar ARMA Power Spectrum Estimation Method to Detect Moisture Content and Compactness Values in Sandy Loam. J. Appl. Geophys. 120:26-35.
Flor O., Palacios H., Suarez F., Salazar K., Reyes L., M., Jimenez K. 2022. New Sensing Technologies for Grain Moisture. Agriculture-Basel. 12:1-26.
Gibson A.P. 2007. A method of determining the moisture content of bulk wheat grain. J. Food Eng. 78:1155-8.
Hemhirun S., Bunyawanichakul P. 2020. Effect of the initial moisture content of the paddy drying operation for the small community. J. Agric. Eng. 51: 176-83.
Huisman J.A., Hubbard S.S., Redman J.D., Annan A.P. 2003. Measuring Soil Water Content with Ground Penetrating Radar: A Review. Vadose Zone J. 2:476-91.
Kandala C.V.K., Leffler R.G., Nelson S.O. 1987. Capacitive Sensors for Measuring Single-kernel Moisture Content in Corn. ASABE. 30:793-7.
Khanshan A.H., Amindavar H., Bakhshi H. 2010. High-Resolution ARMA Estimation of Mixed Spectra. IEEE Trans. Signal Process. 58:97-107.
Kim K.B., Kim J.H., Lee C.J., Noll S.H., Kim M.S. 2006. Simple Instrument for Moisture Measurement in Grain by Free-Space Microwave Transmission. Trans. ASABE. 49:1089-93.
Lewis M.A., Trabelsi S., Nelson S.O. 2019. Development of an Eighth-Scale Grain Drying System with Real-Time Microwave Monitoring of Moisture Content. Appl. Eng. Agric. 35:767-74.
Li Z.H. 1997. Some New Theories and Approaches of ARMA Series and It's Spectral Estimation. J. Dalian Maritime University. 23:93-7.
Lian F.Y., Li Q., Qin Y. 2012. Radar Tomography Detection for Abnormal Regions of Grain Storage Moisture Content. Comput. Eng. 38:198-202.
Liang X.Y., Ji H.Y. 2006. Applicatios of Near Infrared Spectroscopy Technology in Analyzing the Quality of Crops. Chinese Agric. Sci. Bull. 22:366-71.
Lim M.C., Lim K.C., Abdullah M.Z. 2003. Rice Moisture Imaging using Electromagnetic Measurement Technique. Food Bioprod. Process. 81:159-69.
Lunt I.A., Hubbard S.S., Rubin Y. 2005. Soil Moisture Content Estimation using Ground-Penetrating Radar Reflection Data. J. Hydrol. 307:254-69.
Nelson S.O., Trabelsi S., Kraszewski A.W. 2001. RF Sensing of Grain and Seed Moisture Content. IEEE Sens. J. 1:119-26.
Ramli N.M., Rahiman M.H., Kamarudin L.M., Mohamed L., Zakaria A., Ahmad A., Rahim R.A. 2021. A New Method of Rice Moisture Content Determination Using Voxel Weighting-Based from Radio Tomography Images. Sensors. 21:1-19.
Wang J.P., Wang J.M., Zhang Y.F. 2021. Soil Characteristics Measurements with Ground Penetrating Radar: A Review. Chinese J. Soil Sci. 52:242-52.
Weihermuller L., Huisman J.A., Lambot S., Herbst M., Vereecken H. 2007. Mapping the Spatial Variation of Soil Water Content at The Field Scale with Different Ground Penetrating Radar Techniques. J. Hydrol. 340:205-16.
Wu Z.Y., Xia T.X., Nie J.L., Cui F. 2020. The Shallow Strata Structure and Soil Water Content in A Coal Mining Subsidence Area Detected by GPR and Borehole Data. Environ. Earth Sci. 79:1-13.
Zhang X.D. 1999. Modern signal processing. Tsinghua University Press, Beijing, China.
Zheng J., Teng X.Z., Liu J., Qiao X. 2019. Convolutional Neural Networks for Water Content Classification and Prediction with Ground Penetrating Radar. IEEE Access. 7:185385-92.
Zhou Yang., Leng Y.B., Zhao S.L. 2003. Research Progress in GPR Technology Applied in Pavement Testing. Prog. Geophys. 18:481-6.
Zhu J., Zang X.Y., Li T.X. 2021. China's Food Security Risks and Prevention Strategy Under the New Development Pattern. Chinese Rural Econ. 9:2-21.
How to Cite
Cui, F. (2022) “Application of ground penetrating radar technology in moisture content detection of stored grain”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1472.
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